Adaptive sensor performance based on risk assessment

ABSTRACT

Methods to conserve a sensor&#39;s power include training a model to predict a condition of an animal with a training data set, wherein the training data set comprises known outcomes associated with behavioral data and health data for a plurality of animals; sensing, with a sensor worn by a monitored animal, behavioral data and health data of the monitored animal; inputting the behavioral data and health data of the monitored animal into the model; predicting a condition of the monitored animal with the model based on the inputted behavioral data and health data; and configuring a parameter of the sensor associated with the monitored animal based on the predicted condition.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of the following U.S. patentapplication which is incorporated by reference in its entirety: U.S.patent application Ser. No. 16/443,404 (FJEN-0002-U01), filed Jun. 17,2019.

U.S. Ser. No. 16/443,404 (FJEN-0002-U01) claims the benefit of priorityto U.S. Provisional Patent Application 62/686,890 (FJEN-0001-P01), filedon Jun. 19, 2018, entitled “FARM ASSET TRACKING, MONITORING, ANDALERTS.”

The foregoing applications are incorporated herein by reference in theirentirety.

BACKGROUND Field

System, methods, and devices for monitoring and managing livestock andother farm-related assets are disclosed.

Many of the current solutions for monitoring and managing assets onworking farms, particularly small working farms have disadvantagesprimarily arising from the unique needs of the small farm operator. Thesmall farm often has unique aspects requiring management, such as themonitoring of various and different types of livestock e.g. cattle,dairy cows, horses, sheep, goats, etc.) monitoring. Detection andretrieval of lost farm animals is also a concern. Another example isworkflow automation and compliance workflow rules. The list of challengegoes on, but most challenges are related to the fact that small farmsare highly idiosyncratic in their needs and the small farm operator isusually short on time and resources. As such, there are strong barriersfor adoption of technological solutions to monitor and managefarm-related assets. Regarding a small farm's unique needs andcharacteristics, many small farms have transient staffing, tight profitmargins, and, for many small farmers, a low tolerance for technologysetup and maintenance. Some technology barriers include sparseconnectivity across a farm, devices on animals can negatively impactanimal comfort, safety, and productivity, devices on animals may beeasily damaged or lost given potential extremes in the operatingenvironment (temperatures, snow, precipitation, water, mud, and thelike), limited GNSS (GPS) coverage inside farm buildings, limited accessto AC power, and the like and non-technological barriers Thus, there isa need for robust technology solutions that address the abovechallenges, which are reasonably affordable to the small farmer and thatare relatively easy to provision out of the box.

SUMMARY

Barriers to adoption of technology to monitor and manage livestock andfarm assets may include technology barriers such as sparse connectivityacross a farm; the negative impacts of placing devices on animals suchas to comfort, safety, and productivity; the fact that devices onanimals may be easily damaged or lost given potential extremes in theoperating environment (temperatures, snow, precipitation, water, mud,and the like); limited GNSS (GPS) coverage inside farm buildings;limited access to AC power; and the like. Barriers to adoption may alsoinclude non-technological barriers such as limited working capital,transient staffing, tight profit margins, and, for many small farmers, alow tolerance for technology setup and maintenance.

In recognition of these barriers to adoption, the disclosed systems,methods and apparatus provide many features at a lower cost than theexisting systems and in a way that allows a farmer to perform aself-installation and to start with a small, low cost implementation.This initial implementation may comprise software and the application ofmounts and tags, which are small, low cost and easy to replace, to thefarm animals. Potentially, no additional infrastructure would berequired upfront. The system designed to be modular and scalable andthus may then be enlarged gradually over time if needed or modified ifneeded. In embodiments, in the initial installation the disclosed tagsmay work using a user's smartphone or other existing device capable ofnear field/RFID communication and cellular communication. Recentlyreleased standards for Bluetooth mesh communications together withlow-cost data plans operating at low power, low data rates and longintervals facilitate the creation of an infrastructure without the needto install a power-hungry and setup/security intensive WiFi network orproprietary hubs/gateways.

The disclosed methods and systems may function when animals are on theroad or at an unfamiliar location (e.g. a show, fair, race and thelike).

Provisioning a sensor tag for monitoring or managing an animal mayinclude providing a mount for the sensor tag, wherein the mount isadapted to be worn on a body part of an animal and comprises an RFIDdevice, providing the sensor tag that is releasably connectable to themount, exciting the RFID device, wherein the RFID device is programmedwith data of an animal, and upon exciting the RFID device, such as withan application executing on a smartphone, configuring the sensor tag tobe associated with the animal based on the data of the animal. Excitingmay also associate the RFID device and the sensor tag with a useraccount. Configuring may relate to a type of sensor tag (e.g. specificto an animal species, specific to a mounting location on the animal),setting a motion sensing threshold, setting a communications interval,setting a parameter to sense, or the like. Data of the animal may be atleast one of an animal type, a gender, an age, a weight, a feedingprotocol, a medication protocol, a health status, an owner, or a plan ofcare.

In an aspect, a method may include programming a radio device affixed toan asset with an asset information, interrogating the radio device and asensor tag releasably associated with the radio device contemporaneouslyto associate the radio device with the sensor tag, and configuring thesensor tag based on the asset information. Asset information may includean instruction for configuring the sensor tag. The sensor tag mayinclude a transceiver configured to transmit sensed data to at least oneof a repeater, a gateway, a smartphone, or a remote location.

In an aspect, a system for monitoring or managing livestock on a farmmay include a wearable mount adapted to be worn on an animal, thewearable mount comprising a housing and an RFID device within thehousing being programmable with identification data of the animal, asensor tag releasably connectable to the wearable mount, the sensor tagcomprising identification data and adapted to generate data regarding aparameter of the animal when the sensor tag is connected to the wearablemount, an application for monitoring livestock, the application beingaccessible with a mobile device, programmed to: monitor the animal basedat least in part on the parameter of the animal, cause the mobile deviceto interrogate the RFID device, such as with an application executing ona smartphone, and upon interrogation of the RFID device, obtain theidentification data of the animal, cause the mobile device toelectronically retrieve the identification data of the sensor tag, andprovision the sensor tag by associating the sensor tag with the animalbased on the identification data of the sensor tag and theidentification data of the animal. The mobile device may be a phone andthe application may be cloud-based. The application may be in electroniccommunication with a data storage device, wherein the application storesdata of the provisioned sensor. Provisioning may associate the RFIDdevice and the sensor tag with a user account. Provisioning may relateto a type of RFID device (e.g. specific to an animal species, specificto a mounting location on the animal), setting a motion sensingthreshold of the sensor tag, setting a communications interval of thesensor tag, setting a parameter to sense, or the like. Data of theanimal may be at least one of an animal type, a gender, an age, aweight, a feeding protocol, a medication protocol, a health status, anowner, or a plan of care.

In an aspect, a system may include a radio device affixed to an assetprogrammed with an asset information, and an application for monitoringassets, the application being accessible with a mobile device,programmed to: cause a mobile device to interrogate the radio device anda sensor tag releasably associated with the radio devicecontemporaneously to associate the radio device with the sensor tag, andconfigure the sensor tag based on the asset information. The assetinformation comprises an instruction for configuring the sensor tag. Thesensor tag comprises a transceiver configured to transmit sensed data toat least one of a repeater, a gateway, a smartphone, or a remotelocation.

In an aspect, a method of configuring a mesh network for monitoring andmanaging livestock, the mesh network comprising a plurality of radionodes, wherein at least one of the plurality of radio nodes is attachedto an animal, the method including providing a user interface having agraphical display displaying a visual depiction of a geographical area(e.g. a farm), wherein the visual depiction is a map comprisingtopographic features (e.g., landforms and terrain, buildings, roads,fences, walls and other manmade features, vegetation) of thegeographical area; for each radio node of the plurality of radio nodes,identifying a potential placement site on the map; evaluating aperformance of the mesh network by predicting the performance of themesh network, the mesh network having the potential placement site foreach radio node, based at least on the topographic features and dataregarding the animal; utilizing the predicted performance to generate arecommended placement site for each radio node of the plurality of radionodes; and displaying on the graphical display the recommended placementsite for each radio node of the plurality of radio nodes on the map. Viathe graphical display, the method may include altering a topographicfeature (e.g. an addition of a manmade object to the map) in the visualdepiction and re-evaluating the performance of the mesh network based onthe alteration. Evaluating the performance of the mesh network may be bypredicting the performance of the mesh network having the potentialplacement site for each radio node based at least on the topographicfeatures and data regarding the animal further comprises accessing adata store comprising interference profiles for topographic features oranimals. The interference profiles of animals may include a roamingbehavior, a mass, and a herding behavior. The manmade object may be oneof a fence line, a feeder, a trough, a waterer, a farmhouse, a pole, abarn, a corral, a pasture, a shed, a shelter, or a henhouse. Theplurality of radio nodes may include at least one of a repeater, agateway, a sensor tag, a sensor, or a beacon. The potential placementsite for each radio node may include one or more of a fence line, afeeder, a trough, a waterer, a farmhouse, a pole, a barn, a corral, apasture, a shed, a shelter, or a henhouse. Identifying the potentialplacement site for each radio node in the map involves identifying apotential signal obstruction on the map. Generating involves a manualinput from a user of features of the geographical area. The method mayfurther include, via the graphical display, altering the potentialplacement site of at least one of the plurality of radio nodes andre-evaluating the performance of the mesh network to determine aresilience of the mesh network to the alteration, wherein altering thepotential placement site of the at least one of the plurality of radionodes comprises altering a portion of the plurality of radio nodes thatare attached to animals. Altering the portion of the plurality of radionodes that are attached to animals includes one of altering a number ofanimals or altering a geographic location of the animals. The sensor tagmay be attached to an animal. The sensor may generate data indicative ofa movement of the animal or a physiological parameter of the animal.

In an aspect, a system for monitoring and managing livestock may includea mesh network of a plurality of radio nodes, wherein at least one ofthe plurality of radio nodes is attached to an animal, a user interfacehaving a graphical display displaying a visual depiction of ageographical area (e.g. a farm), wherein the visual depiction is a mapcomprising topographic features of the geographical area; a map managerthat identifies a potential placement site on the map for each radionode of the plurality of radio nodes; and a prediction facility thatpredicts a performance of the mesh network having the potentialplacement site for each radio node based at least on the topographicfeatures and data regarding the animal, and generates a recommendedplacement site for each radio node of the plurality of radio nodes basedon the predicted performance, wherein the recommended placement site foreach radio node of the plurality of radio nodes on the map is displayedon the graphical display. The user interface may receive, from one ormore input devices, an input comprising alteration of a topographicfeature in the visual depiction, wherein the prediction facilityre-evaluates the performance of the mesh network based on thealteration. The alteration may include an addition of a manmade objectto the map. The system may further include a data store comprisinginterference profiles for topographic features or animals that isaccessed by the prediction facility in predicting the performance. Theinterference profiles of animals may include a roaming behavior, a mass,and a herding behavior. The manmade object may be one of a fence line, afeeder, a trough, a waterer, a farmhouse, a pole, a barn, a corral, apasture, a shed, a shelter, or a henhouse. The plurality of radio nodesmay include at least one of a repeater, a gateway, sensor, a sensor tag,or a beacon. The potential placement site for each radio node includesone or more of a fence line, a feeder, a trough, a waterer, a farmhouse,a pole, a barn, a corral, a pasture, a shed, a shelter, or a henhouse.The map manager may further identify a potential signal obstruction onthe map. The user interface may receive, from one or more input devices,one or more features of the geographical area, or an input comprisingalteration of the potential placement site of at least one of theplurality of radio nodes, wherein the prediction facility re-evaluatesthe performance of the mesh network to determine a resilience of themesh network to the alteration. The alteration may be of a portion ofthe plurality of radio nodes that are attached to animals, such as byaltering a number of animals or altering a geographic location of theanimals. The sensor tag, attached to an animal, may generate dataindicative of a movement of the animal or a physiological parameter ofthe animal.

In an aspect, a method for provisioning a working farm with a monitoringsystem may include providing a plurality of radio nodes to be used in amesh network; providing an electronic interface through which a userinputs data regarding one or more topographical features of the workingfarm and animals on the working farm; with a computer processor,accessing an electronic data store of obstruction profiles of the one ormore topographical features and obstruction profiles of the animals onthe working farm; with a computer processor, predicting a performance ofthe mesh network based on the obstruction profiles of the one or moretopographical features and the obstruction profiles of the animals;generating a recommendation for placement of the plurality of radionodes; and causing the electronic interface to output the recommendationfor placement. The obstruction profiles of the animals may includeroaming behavior, mass, or herding behavior. The step of generating therecommendation for placement of the plurality of radio nodes includes arecommendation of placement on an animal. The recommendation ofplacement on the animal may include a recommendation of a body part ofthe animal on which to place the plurality of radio nodes. The sensortag may be attached to an animal and generates data indicative of amovement of the animal or a physiological parameter of the animal. Theplurality of radio nodes may include at least one of a sensor, sensortag, repeater, a gateway, a tag, or a beacon. The recommendation forplacement for each radio node of the plurality of radio nodes comprisesone or more of a fence line, a feeder, a trough, a waterer, a farmhouse,a pole, a barn, a corral, a pasture, a shed, a shelter, or a henhouse.The method may further include, via the electronic interface, alteringthe recommendation for placement of at least one of the plurality ofradio nodes and re-evaluating the performance of the mesh network todetermine a resilience of the mesh network to the alteration.

In an aspect, a system for provisioning a working farm with a monitoringsystem may include a plurality of radio nodes to be used in a meshnetwork; an electronic interface through which a user inputs dataregarding one or more topographical features of the working farm andanimals on the working farm; a computer processor that accesses anelectronic data store of interference profiles of the one or moretopographical features and obstruction profiles of the animals on theworking farm; a computer processor that predicts a performance of themesh network based on the obstruction profiles of the one or moretopographical features and the interference profiles of the animals; anda recommendation engine that generates a recommendation for placement ofthe plurality of radio nodes, wherein the electronic interface outputsthe recommendation for placement. The obstruction profiles of theanimals may include roaming behavior, mass, or herding behavior. Therecommendation for placement of the plurality of radio nodes may includea recommendation of placement on an animal. The recommendation ofplacement on the animal may include a recommendation of a body part ofthe animal on which to place the plurality of radio nodes. The sensortag may be attached to an animal and may generate data indicative of amovement of the animal or a physiological parameter of the animal. Theplurality of radio nodes may include at least one of a repeater, agateway, a tag, a sensor, or a beacon. The recommendation for placementfor each radio node of the plurality of radio nodes may include one ormore of a fence line, a feeder, a trough, a waterer, a farmhouse, apole, a barn, a corral, a pasture, a shed, a shelter, or a henhouse. Theuser may input, through the electronic interface, an alteration to therecommendation for placement of at least one of the plurality of radionodes, and wherein based on the alteration, the computer processorre-evaluates the performance of the mesh network to determine aresilience of the mesh network to the alteration.

In an aspect, a computer-implemented method to create a layout for aplurality of radio nodes in a mesh network to manage or monitorlivestock on a farm having topographic features and animals may includeobtaining feedback data for a plurality of radio nodes placed inselected geographical areas, wherein the feedback data relates to aperformance indication of the plurality of radio nodes, whereinperformance is at least partially determined by assessing an effect thetopographic features in the selected geographical areas and the animalshave on performance; utilizing the feedback data to select a trainingdata set for a model for placing radio nodes in a geographical area,wherein the training data set comprises aspects of a plurality ofplacements of the plurality of radio nodes that yielded performancemeasures that exceed a threshold; training the model with the trainingdata set, wherein the training data set comprises information aboutradio node placements, an obstruction profile of the animals ortopographical features, and data on a mesh network performance;proposing a placement of a new plurality of radio nodes to form a newmesh network in a new geographical area by identifying one or morepotential placement sites in a topographical map of the geographicalarea; and using the model, estimating a performance of the new meshnetwork and outputting the estimated performance on the topographicalmap. The step of proposing the placement of the new plurality of radionodes to form the new mesh network in the new geographical area byidentifying one or more potential placement sites in the topographicalmap of the geographical area further includes proposing a placement ofat least one node on at least one of the animals. The feedback data mayfurther be utilized to select a testing data set for the model, andtesting the model with the testing data set. The method may furtherinclude altering at least one of the one or more potential placementsites of at least one of the new plurality of radio nodes andre-estimating the performance of the new mesh network to determine aresilience of the mesh network to the alteration, such as by altering aportion of the new plurality of radio nodes that are attached to animals(e.g. by altering a number of animals or altering a geographic locationof the animals).

In an aspect, a method of operating a power-efficient ad-hoc meshnetwork may include positioning at least two wireless transceivers nearan electric fence, wherein the electric fence is energized in accordancewith a pulsed discharge interval; detecting a pulsed discharge the atleast two wireless transceivers; and synchronizing a shared transmissiontiming window of the at least two wireless transceivers with thedetected pulse discharge, wherein when a timing is not inside the sharedtransmission timing window, at least one of the at least two wirelesstransceivers enters a low power mode wherein transmitting or receivingis not possible. Detecting a pulsed discharge may include detecting achange in an electro-magnetic field near the electric fence. The methodmay further include triggering an alert when at least one of the atleast two wireless transceivers: detects a lack of fence discharges,discontinues synchronized transmissions, or transmits continuously. Analert may indicate one of a break in a fence or a fence outage. When thealert is triggered, the method identifies an asset last located near theat least two wireless transceivers. The method may further includeincreasing a tracking interval of the asset during a time that the atleast two wireless transceivers at least one of detects a lack of fencedischarges or discontinues synchronized transmissions or transmitscontinuously. A non-contact means of detecting pulsed discharges from anelectro-magnetic field produced by the pulsed discharge may result inthe at least two wireless transceivers waking up. The at least twowireless transceivers may be powered by energy harvested from theelectric fence. The at least two wireless transceivers may be configuredto transmit and receive during a respective communications timing windowof two different electric fences, with potentially different dischargeintervals, bridging communications between these two networks. Inembodiments, at least one of the two wireless transceivers may operateduring the timing windows of both the first and second fence. Thiswireless transceiver might then bridge communications to a thirdtransceiver operating only on the second fence.

In an aspect, a system for monitoring or managing livestock on a farmmay include a wearable sensor adapted to generate data regarding aparameter of an animal when the wearable sensor is worn by the animal ata sensing interval; a processor in electronic communication with thewearable sensor that receives the data regarding the parameter of theanimal at a communication interval, the processor programmed to: assessa health risk for the animal based on data generated by the wearablesensor, and generate instructions to modify at least one of the sensinginterval of the wearable sensor or the communication interval based onthe health risk. The communication interval may be an interval at whichthe wearable sensor transmits the data regarding the parameter of theanimal or the processor receives the data regarding the parameter of theanimal. The system may further include a mobile device at least one ofcomprising or in electronic communication with the processor, whereinthe mobile device is in electronic communication with the wearablesensor. The mobile device may transmit the instructions to the wearablesensor. The wearable sensor may modify the sensing interval based on theinstructions. The sensing interval may be increased, and the processormay be further programmed to generate instructions to determine if thehealth risk has decreased and to generate and transmit instructions tothe wearable sensor to decrease the sensing interval if the health riskhas decreased. The sensing interval may be decreased, and the processormay be further programmed to generate instructions to determine if thehealth risk has increased and to generate and transmit instructions tothe wearable sensor to increase the sensing interval if the health riskhas increased. The wearable sensor may be releasably attached to a mountpermanently affixed to the animal, and may generate data indicative ofat least one of a movement, a physiological parameter of the animal, ora behavior of the animal. The wearable sensor may generate dataindicative of an animal body function comprising at least one of aurination, a respiration, a lactation, a bowel movement, a bodymeasurement, a calving activity, or a passing gas. The behavioral datamay relate to at least one of a grazing habit, a grazing pattern, afeeding duration, a rumination, a drinking habit, a migration pattern, asleeping schedule, a lying time, a reproductive activity, a congregationactivity, or a proximity to another animals/stationary device.

In an aspect, a method for monitoring livestock on a farm may includeproviding a wearable sensor to be worn on an animal; generating data ofa parameter of the animal when the wearable sensor is worn by the animalat a sensing interval; electronically transmitting the data of theparameter of the animal at a communication interval to a server; withthe server, determining a health risk of the animal based on the data ofthe parameter of the animal; with the server, generating instructions tomodify at least one of the sensing interval or the communicationinterval based on the health risk; and modifying at least one of thecommunication interval or the sensing interval by transmitting theinstructions to the wearable sensor. The instructions to modify at leastone of the sensing interval of the wearable sensor or the communicationinterval based on the health risk may include instructions to increaseor decrease at least one of the sensing interval of the wearable sensoror the receiving interval. The wearable sensor may be releasablyattached to a mount permanently affixed to the animal, and may generatedata indicative of a movement of the animal, a physiological parameterof the animal, an animal body function comprising at least one of aurination, a respiration, a lactation, a bowel movement, a bodymeasurement, a calving activity, or a passing gas, or a behavior of theanimal. The behavioral data may relate to at least one of a grazinghabit, a grazing pattern, a feeding duration, a rumination, a drinkinghabit, a migration pattern, a sleeping schedule, a lying time, areproductive activity, a congregation activity, or a proximity toanother animals/stationary device.

In an aspect, a computer-implemented method to conserve a wearablesensor's power may include training a model with a training data set forpredicting a condition of an animal, wherein the training data setcomprises known outcomes associated with behavioral data and health datafor a plurality of animals; training the model with the training dataset to obtain a trained model; predicting a condition of an animal byinputting current behavioral data and current health data to the trainedmodel; and tailoring a parameter of a wearable sensor for an animalbased on the predicted condition. The parameter may be a sensitivity ofthe wearable sensor, a measurement interval of the wearable sensor, acommunications power or a communications interval. The parameter may befurther tailored based on at least one of: a predicted wearable sensorbattery life, a mesh network performance or received signal strength ofthe wearable sensor, proximity of the animal to a point of interest orproximity to a suspected break in containment, a desire to activate anactuator in proximity to the animal wearing the wearable sensor.

In an aspect, a system for monitoring or managing livestock on a farmmay include a plurality of wearable sensors in a mesh network, each ofthe plurality of wearable sensors in the plurality of wearable sensorsadapted to generate data regarding location and at least one of apositional parameter or a behavioral parameter of an animal wearing oneof the plurality of wearable sensors; a processor in electroniccommunication with a first wearable sensor of the plurality of wearablesensors worn by a first animal and a second wearable sensor of theplurality of wearable sensors worn by a second animal, the processorprogrammed to: determine a transmission impairment of the first wearablesensor based on at least one of the positional parameter or thebehavioral parameter of the second animal, and generate instructions tomodify a transmission characteristic of the mesh network based on thetransmission impairment, communicate the instructions to at least oneother wearable sensor of the plurality of wearable sensors, and modifythe transmission characteristic of at least one other wearable sensor ofthe plurality of wearable sensors based on the instructions. Theinstructions to modify the transmission characteristic may includemodifying a transmission rate or a power of a signal transmitted by atleast one of the wearable sensors of the plurality of wearable sensors.The behavioral parameter may relate to at least one of a grazing habit,a grazing pattern, a feeding duration, a rumination, a drinking habit, amigration pattern, a sleeping schedule, a lying time, a reproductiveactivity, a congregation activity, or a proximity to anotheranimals/stationary device. The positional parameter may relate to atleast one of a head position, a body position, a body elevation, a bodyorientation, a movement, or a stance.

In an aspect, a computer-implemented method may include providing afirst electronic sensor to be worn by a first animal that generates dataof a physiological or a behavioral parameter of the first animal whenworn by the first animal; providing a second electronic sensor to beworn by a second animal that generates data of a physiological parameteror a behavioral parameter of the second animal when worn by the secondanimal; determining a proximity of a first electronic sensor to thesecond electronic sensor if the second electronic sensor is associatedwith a location information; and establishing a membership of the firstanimal in a herd based on the determined proximity and at least one of(i) the data of the physiological parameter of the first animal, (ii)the data of the behavioral parameter of the first animal, (iii) the dataof the physiological parameter of the second animal, or (iv) the data ofthe behavioral parameter of the second animal. The behavioral parametermay relate to at least one of a grazing habit, a grazing pattern, afeeding duration, a rumination, a drinking habit, a migration pattern, asleeping schedule, a lying time, a reproductive activity, a congregationactivity, or a proximity to another animals/stationary device. Thephysiological parameter may relate to an animal body function comprisingat least one of a urination, a respiration, a lactation, a bowelmovement, a body measurement, a calving activity, or a passing gas.

In an aspect, a method for determining a location of a radio device wornby an animal may include identifying currently available methods fordetermining the location of the radio device, wherein the currentlyavailable methods comprise at least one of: determining a proximity to asmartphone with a known location, determining a proximity to a fixedlocation beacon, determining a proximity to another radio device whichhas a high confidence in its own location, or triangulating (angles) ormultilaterating (ranges) from a set of at least three location anchorsat known locations, wherein the radio device selects which of thecurrently available methods should be used and at what update rate basedon one or more factors. The one or more factors may include a time sincethe radio device was last located, a confidence in the location of theradio device, a current condition of the animal bearing the radiodevice, urgency to activate an actuator in proximity to the animal, or atime of day. Identifying the currently available methods may includeidentifying the smartphone within range, identifying the set of at leastthree location anchors within range, identifying another radio device oridentifying a fixed location beacon within range. Determining aproximity to a fixed location beacon may include listening for one ormore fixed location beacons; recording a signal strength or angle toeach fixed location beacon detected; and forwarding the recorded signalstrength for each fixed location beacon to a remote computer forprocessing, wherein the remote computer maintains a physical location ofthe one or more fixed location beacons. If the radio device detects thefixed location beacon at a signal strength that exceeds a threshold,triangulating/multilaterating may not be additionally selected fordetermining the location of the radio device. If the radio device doesnot detect any fixed location beacons, the location of the radio devicemay be determined using a location of the smartphone in proximity. Themethod may further include calculating a confidence circle based on asignal strength between the radio device and the smartphone. When thelocation of the radio device is outside of a boundary, the radio devicemay be triggered to at least one of expend additional energy insubsequent location attempts, reduce a location update interval, oractivate an emergency locating system.

In an aspect, a method may include determining a proximity of a firstradio device associated with a first asset to a second radio deviceassociated with a second asset which has a high confidence in its ownlocation; estimating, based on the proximity, location of the secondradio device, and a partial location estimate for the first radio deviceusing two or more fixed location anchors, that the second asset isobstructing a signal between the first radio device and a third locationbeacon; and multilaterating a location of the first radio device from aset of at least two location anchors at known locations and a signalstrength to the second radio device, thereby ignoring the signalstrength of the obstructed third location beacon. The first and secondradio devices may be a sensor tag worn by an animal. The third locationbeacon may be located on one of a fence line, a feeder, a trough, awaterer, a farmhouse, a pole, a barn, a corral, a pasture, a shed, ashelter, or a henhouse.

In an aspect, a method may include determining a proximity of a firstradio device associated with a first asset to a second radio deviceassociated with a second asset which has a high confidence in its ownlocation; estimating, based on the proximity and a location of thesecond radio device and a partial location estimate for the first radiodevice, using two or more fixed location beacons, that the second assetis obstructing a signal between the first radio device and a fixedlocation beacon; and refining a multilateration of a location of thefirst radio device by substituting a series of corrected signalstrengths for the obstructed fixed location beacon, and identifying thelocation of the first radio device by a strong correlation between therefined estimated location and the proximity of the second radio device.The first and second radio device may be a sensor tag worn by an animal.The series of corrected signal strengths may take into account a postureor an orientation of the first asset, wherein the first asset is a firstfarm animal and the second asset is a second farm animal. The series ofcorrected signal strengths may take into account a posture or anorientation of the second asset with respect to a path to the fixedlocation beacon that it is obstructing. A perpendicular orientation ofthe second asset relative to the fixed location beacon may require amore significant correction than a parallel or an oblique orientation.The fixed location beacon may be located on one of a fence line, afeeder, a trough, a waterer, a farmhouse, a pole, a barn, a corral, apasture, a shed, a shelter, or a henhouse.

In an aspect, a method for estimating a location of a radio device, mayinclude determining a set of characteristics of the radio device: aproximity to a smartphone with a known location, a proximity to a fixedlocation beacon, a proximity to another radio device which has a highconfidence in its own location, and a triangulation/multilateration froma set of at least three location anchors at known locations; determininga relative confidence of one or more estimated locations based on atleast one of set of characteristics; and graphically depicting a rangeof possible locations of the radio device to a user based on the one ormore estimated locations and the relative confidence. When the locationof the radio device is outside of a boundary, the radio device may betriggered to at least one of expend additional energy in subsequentdetermining the set of characteristics, reduce a location updateinterval, or activate an emergency locating system. The radio device isa sensor tag worn by an animal. The fixed location beacon may be locatedon one of a fence line, a feeder, a trough, a waterer, a farmhouse, apole, a barn, a corral, a pasture, a shed, a shelter, or a henhouse.

In an aspect, a method for estimating a location of a radio device mayinclude determining a set of characteristics of the radio device,comprising: a proximity to a smartphone with a known location, aproximity to a fixed location beacon, a proximity to another radiodevice which has a high confidence in its own location, and atriangulation/multilateration from a set of at least three locationanchors at known locations; determining a relative confidence of one ormore estimated locations based on at least one of the set ofcharacteristics; and adjusting, based on the relative confidence, anenergy expended in determining the set of characteristics. When the oneor more estimated locations are outside of a boundary, the radio devicemay be triggered to at least one of reduce a location update interval oractivate an emergency locating system. The radio device is a sensor tagworn by an animal. The fixed location beacon may be located on one of afence line, a feeder, a trough, a waterer, a farmhouse, a pole, a barn,a corral, a pasture, a shed, a shelter, or a henhouse.

In an aspect, a method may include sensing a position of a portion of ananimal using a sensor tag associated with a radio device; predicting ifa signal from the radio device is obstructed based on the position; andaugmenting the signal if the signal is predicted to be obstructed.Augmenting the signal may include increasing a communication power ofthe sensor tag. The signal may be obstructed if the position indicatesthat an animal's head is close to a ground. The signal augmentation maybe determined based on the position of the animal's head. The sensor tagmay be one or more of an inclinometer, magnetometer, an accelerometer,gyroscope, a barometer or a GPS. The position may relate to at least oneof a head position, a body position, a body orientation, a bodyelevation, a movement, or a stance.

In an aspect, a computer-implemented method to map signal obstructionsof a mesh network to monitor and manage animals in a geographical areaon a farm may include determining a location for a plurality of radionodes affixed to animals in a geographical area; identifying when asignal from one of the plurality of radio nodes is obstructed andcorrelating an obstruction with the location of the one of the pluralityof radio nodes; and updating a graphical representation of the meshnetwork with a representation of the obstruction. The step ofidentifying when the signal from one of the plurality of radio nodes isobstructed may be done by determining at least one of a behavioralcharacteristic or a positional characteristic of at least one of theanimals on the farm. The behavioral characteristic may relate to atleast one of a grazing habit, a grazing pattern, a feeding duration, arumination, a drinking habit, a migration pattern, a sleeping schedule,a lying time, a reproductive activity, a congregation activity, or aproximity to another animals/stationary device. The positionalcharacteristic may relate to at least one of a head position, a bodyposition, a body elevation, a movement, or a stance. The graphicalrepresentation may be a topographical map of the geographical area.Determining the location may be by at least one of determining aproximity to a smartphone with a known location, determining a proximityto a fixed location beacon, determining a proximity to another radiodevice which has a high confidence in its own location, or triangulatingfrom a set of at least three location anchors at known locations.

In an aspect, a system for estimating a location of a radio device mayinclude a plurality of mobile devices, each with a known location andcapable of detecting a radio device; a remote server for aggregatingdata from the plurality of mobile devices, wherein when one of theplurality of mobile devices detects the radio device, a detection recordis generated and transmitted to the remote server; and a graphicaldisplay of a user application that depicts an estimated location of theradio device based on the detection record. The detection record mayinclude at least one of a time, an identifier, and a location. Theplurality of mobile devices may be associated with different useraccounts. The radio device may be associated with a user account. Anymobile device of the plurality of mobile devices may detect the radiodevice regardless of whether the mobile device is associated with theuser account that is associated with the radio device.

In an aspect, a system for estimating a location of a radio device mayinclude a plurality of a wearable sensors adapted to be worn by ananimal and capable of detecting a presence of one or more mobiledevices; wherein when one of the plurality of wearable sensors detects amobile device of the one or more mobile devices, a detection record isgenerated and transmitted to a remote server; the remote server foraggregating data from the plurality of wearable sensors regarding thedetected mobile devices; and a graphical display of a user applicationthat depicts an estimated location of the one of the plurality ofwearable sensors based on the detection record. The detection record mayinclude at least one of a time, an identifier, and a location. Theplurality of wearable sensors may be associated with different useraccounts. The mobile device may be associated with a user account. Anywearable sensor of the plurality of wearable sensors may detect themobile device regardless of whether the wearable sensor is associatedwith the user account that is associated with the mobile device.

In an aspect, a method to determine workflow events on a farm mayinclude obtaining sensor data from one or more animals, wherein thesensor data relate to a behavior or a location of the one or moreanimals; and inferring a workflow event based on the sensor data. Themethod may further include logging the workflow event in a compliancelog. When the sensor data indicate that the location of the one or moreanimals is in a field, an inferred workflow event may be that a gate wasopened. When the sensor data indicate that the one or more animals arecongregating near other animals and the one or more animals have theirheads down, an inferred workflow event may be that the one or moreanimals were fed. When the sensor data indicate that the one or moreanimals are near a feed pan containing a beacon, an inferred workflowevent may be that a correct food or medication was delivered to the oneor more animals.

In an aspect, a method may include obtaining sensor data from one ormore animals, wherein the sensor data relate to a behavior or a locationof the one or more animals; and triggering a workflow event based on thesensor data. When the sensor data indicate a specific condition of theone or more animals and a location of the one or more animals near afeeding/watering location, the workflow event triggered may be adelivery of specific food or medication, or a delivery of water. Whenthe specific condition is that the one or more animals are not drinking,the specific food delivered may be a small portion of desirable feeddusted with salt and/or electrolytes to promote thirst and encouragedrinking.

In an aspect, a computer-implemented method of determining compliancewith workflow rules on a farm may include for each of a plurality ofanimals on the farm, providing a wearable sensor configured to sense aparameter of an animal wearing the wearable sensor; receiving, at aprocessor, the parameters for the plurality of animals; with theprocessor, determining at least one of a location, a behavior, or aposition of the plurality of animals based on the parameters; anddetermining compliance with a workflow rule based on at least one of thelocation, the behavior, or the position of the plurality of animals. Thebehavior may relate to at least one of a grazing habit, a grazingpattern, a feeding duration, a rumination, a drinking habit, a migrationpattern, a sleeping schedule, a lying time, a reproductive activity, acongregation activity, or a proximity to another animals/stationarydevice. The position may be at least one of a head position, a bodyposition, a body elevation, a movement, or a stance. The location may bea fence line, a feeder, a trough, a waterer, a farmhouse, a pole, abarn, a corral, a pasture, a shed, a shelter, or a henhouse. Theworkflow rule may relate to at least one of a stabling, a pasturing, aherding, a sheltering, a feeding, a medicating, a provision of water, amanure and/or wastewater removal, an inspection interval, a recordsmanagement, or a feed storage. A user may input or update the workflowrule.

In an aspect, a method may include obtaining sensor data from one ormore animals, wherein the sensor data relate to a behavior or a locationof the one or more animals; and triggering a workflow event based on thesensor data. When the sensor data indicate a specific condition of theone or more animals and a location of the one or more animals near afeeding/watering location, the workflow event triggered may be adelivery of specific food or medication, or a delivery of water. Theworkflow event may relate to at least one of a stabling, a pasturing, aherding, a sheltering, a feeding, a medicating, a provision of water, amanure and/or wastewater removal, an inspection interval, a recordsmanagement, or a feed storage. A user may input or update the workflowrule.

In an aspect, a system for determining compliance with workflow rules ona farm may include a plurality of wearable sensors, each to be worn by acorresponding animal, each wearable sensor of the plurality of wearablesensors generating data of at least one of a behavior, a location, or aphysiology of the corresponding animal wearing the wearable sensor; adata storage configured to store workflow rules for the farm; aprocessor in communication with the data storage and the plurality ofwearable sensors, the processor programmed to: aggregate the data of theat least one of the behavior, the location, or the physiology of aplurality of corresponding animals to determine a characteristic of theplurality of corresponding animals, and determine a compliance with aworkflow rule based on the characteristic of the plurality ofcorresponding animals. The behavior may relate to at least one of agrazing habit, a grazing pattern, a feeding duration, a rumination, adrinking habit, a migration pattern, a sleeping schedule, a lying time,a reproductive activity, a congregation activity, or a proximity toanother animals/stationary device. The location may be a fence line, afeeder, a trough, a waterer, a farmhouse, a pole, a barn, a corral, apasture, a shed, a shelter, or a henhouse. The workflow rule may relateto at least one of a stabling, a pasturing, a herding, a sheltering, afeeding, a medicating, a provision of water, a manure and/or wastewaterremoval, an inspection interval, a records management, or a feedstorage. The plurality of wearable sensors may generate data regarding aposition of the plurality of corresponding animals. The position may beat least one of a head position, a body position, a body elevation, amovement, or a stance. The system may further include a user interfacefor at least one of inputting or modifying the workflow rule.

In an aspect, a system for triggering a workflow event on a farm mayinclude a plurality of wearable sensors, each to be worn by acorresponding animal, each wearable sensor of the plurality of wearablesensors generating data of at least one of a behavior, a location, or aphysiology of the corresponding animal wearing the wearable sensor; aprocessor in communication with a data storage and the plurality ofwearable sensors, the processor programmed to: aggregate the data of theat least one of the behavior, the location, or the physiology of aplurality of corresponding animals to determine a characteristic of theplurality of corresponding animals, and trigger a workflow event basedon the characteristic of the plurality of corresponding animals. Thebehavior may relate to at least one of a grazing habit, a grazingpattern, a feeding duration, a rumination, a drinking habit, a migrationpattern, a sleeping schedule, a lying time, a reproductive activity, acongregation activity, or a proximity to another animals/stationarydevice. The location may be a fence line, a feeder, a trough, a waterer,a farmhouse, a pole, a barn, a corral, a pasture, a shed, a shelter, ora henhouse. The workflow event may relate to at least one of a stabling,a pasturing, a herding, a sheltering, a feeding, a medicating, aprovision of water, a manure and/or wastewater removal, an inspectioninterval, a records management, or a feed storage. The plurality ofwearable sensors may generate data regarding a position of the pluralityof corresponding animals. The position may be at least one of a headposition, a body position, a body elevation, a movement, or a stance.The system may further include a user interface for at least one ofinputting or modifying the workflow event.

In an aspect, a system for relaying an implantable sensor data mayinclude an implantable sensor comprising a wireless communicationfacility enabled to collect biological data related to an animal; and anexternal wearable device that communicates wirelessly with theimplantable sensor when the implantable sensor is implanted in theanimal, the external wearable device receiving the biological data andrelaying it to a remote location, wherein the external wearable deviceprovides non-contact power to the implantable sensor. The wirelesscommunication facility may utilize near field communication. Theimplantable sensor may be one of implanted, inserted or ingested. Theexternal wearable device may be a sensor tag associated with the animalor a different animal. The external wearable device may relay aconfiguration information from the remote location to the implantablesensor. The system of claim may further include a processor at theremote location in electronic communication with the external wearabledevice that receives the biological data, the processor programmed to:(i) assess a health risk for the animal based on biological datagenerated by the implantable sensor, and (ii) generate instructions tomodify at least one of a sensing interval of the implantable sensor, acommunication interval of the implantable sensor to the externalwearable device, or a relay interval of the external wearable device tothe remote location based on the health risk.

In an aspect, a method for relaying an implantable sensor data mayinclude collecting biological data related to an animal from animplantable sensor comprising a wireless communication facility; andrelaying the biological data to a remote location with an externalwearable device that communicates wirelessly with the implantable sensorwhen the implantable sensor is implanted in the animal, wherein theexternal wearable device provides non-contact power to the implantablesensor. The wireless communication facility may utilize near fieldcommunication. The implantable sensor may be one of implanted, insertedor ingested. The external wearable device may be a sensor tag associatedwith the animal or a different animal. The external wearable device mayrelay a configuration information from the remote location to theimplantable sensor. The method may further include assessing a healthrisk for the animal based on the biological data generated by theimplantable sensor, and generating instructions to modify at least oneof a sensing interval of the implantable sensor, a communicationinterval of the implantable sensor to the external wearable device, or arelay interval of the external wearable device to the remote locationbased on the health risk.

In an aspect, a system to determine whether a farm animal is distressedmay include a first sensor tag generating physiological data orbehavioral data about a first farm animal, wherein the first farm animalis in a herd; a second sensor tag generating physiological data orbehavioral data about a second farm animal contemporaneous with thephysiological data or behavioral data about the first farm animal; aprocessor in electronic communication with the first sensor tag and thesecond sensor tag, the processor programmed to determine whether thefirst farm animal is distressed based on the physiological data aboutthe first farm animal, the behavioral data of the first farm animal, thephysiological data about the second farm animal, and the behavioral dataof the second farm animal. The behavioral data may relate to at leastone of a grazing habit, a grazing pattern, a feeding duration, arumination, a drinking habit, a migration pattern, a sleeping schedule,a lying time, a reproductive activity, a congregation activity, or aproximity to another animals/stationary device. The physiological datamay be obtained by non-invasive detection of an animal body function.The animal body function may be at least one of a urination, arespiration, a lactation, a bowel movement, a body measurement, acalving activity, or a passing gas. The processor may be in furtherelectronic communication with an environmental sensor to collectenvironmental data, and wherein the processor is programmed to determinewhether the first farm animal is distressed based on the physiologicaldata about the first farm animal, the behavioral data of the first farmanimal, the physiological data about the second farm animal, thebehavioral data of the second farm animal, and the environmental data.The environmental data may include at least one of a temperature, ahumidity, a precipitation, a pollen count, an air quality, a weatherevent, a season, a sunrise, a sunset, or a solar irradiation. Theprocessor may be in further electronic communication with a contextualsensor to collect contextual data, and wherein the processor isprogrammed to determine whether the first farm animal is distressedbased on the physiological data about the first farm animal, thebehavioral data of the first farm animal, the physiological data aboutthe second farm animal, the behavioral data of the second farm animal,and the contextual data. The contextual data may include a location, apath, an activity, a time, a date, a relationship, a weather status, orany other information providing a context. The processor may be infurther electronic communication with an environmental sensor to collectenvironmental data and a contextual sensor to collect contextual data,and wherein the processor is programmed to determine whether the firstfarm animal is distressed based on the physiological data about thefirst farm animal, the behavioral data of the first farm animal, thephysiological data about the second farm animal, the behavioral data ofthe second farm animal, the environmental data, and the contextual data.

In an aspect, a system to determine whether a farm animal is distressedmay include a first sensor tag generating behavioral data of a firstfarm animal, wherein the first farm animal is in a herd; a second sensortag generating behavioral data of a second farm animal in the herd ofthe first farm animal, wherein the behavioral data of the second farmanimal is contemporaneous with the behavioral data of the first farmanimal; a contextual sensor to collect contextual data relating to thefirst farm animal and the second farm animal; a processor in electroniccommunication with the first and second sensor tags and the contextualsensor, the processor programmed to determine whether the first farmanimal is distressed based on the behavioral data of the first farmanimal, the behavioral data of the second farm animal, and thecontextual data. The behavioral data may relate to at least one of agrazing habit, a grazing pattern, a feeding duration, a rumination, adrinking habit, a migration pattern, a sleeping schedule, a lying time,a reproductive activity, a congregation activity, or a proximity toanother animals/stationary device. The processor may be in furtherelectronic communication with an environmental sensor to collectenvironmental data, and wherein the processor is programmed to determinewhether the first farm animal is distressed based on the behavioral dataof the first farm animal, the behavioral data of the second farm animal,the contextual data, and the environmental data. The environmental datamay include at least one of a temperature, a humidity, a precipitation,a pollen count, an air quality, a weather event, a season, a sunrise, asunset, or a solar irradiation. The contextual data may include alocation, a path, an activity, a time, a date, a relationship, a weatherstatus, or any other information providing a context.

In an aspect, a computer-implemented method to infer a condition of ananimal in a herd may include obtaining behavioral data and health datafor a plurality of animals in the herd; selecting a subset of thebehavioral data and the health data for use in a training data set totrain a model for predicting a condition of a selected animal in theherd, wherein the training data set comprises known outcomes associatedwith the behavioral data and health data; training the model with thetraining data set to obtain a trained model; electronically sensing acurrent health data of the selected animal in the herd; electronicallyobtaining a current behavioral data of the selected animal in the herd;predicting a condition of the selected animal in the herd by inputtingthe current behavioral data and the current health data into the trainedmodel. The computer-implemented method may further include selecting asubset of the behavioral data and the health data for use in a testingdata set, and testing the trained model with the testing data set.Training the model may further include using environmental data, andknown outcomes associated with the environmental data. The environmentaldata may include at least one of a temperature, a humidity, aprecipitation, a pollen count, an air quality, a weather event, aseason, a sunrise, a sunset, or a solar irradiation. Predicting mayfurther include inputting a current environmental data. Training themodel may further include using contextual data, and known outcomesassociated with the contextual data. Predicting may further includeinputting a current contextual data. The contextual data may include alocation, a path, an activity, a time, a date, a relationship, a weatherstatus, or any other information providing a context. The health dataand the current health data may include vital signs. The behavioral dataand the current behavioral data may relate to a whole herd behavior. Thewhole herd behavior may be a whole herd respiration rate. The whole herdbehavior may be an average of the whole herd respiration rate. The wholeherd behavior may be averaged or normalized. The behavioral data and thecurrent behavioral data may relate to at least one of a grazing habit, agrazing pattern, a feeding duration, a rumination, a drinking habit, amigration pattern, a sleeping schedule, a lying time, a reproductiveactivity, a congregation activity, or a proximity to anotheranimals/stationary device. The health data and the current health datamay be obtained by non-invasive detection of an animal body function.

In an aspect, a system to determine whether a farm animal is distressedmay include a first sensor tag adapted to be worn by or implanted,ingested, or inserted into a first farm animal, the first sensor taggenerating physiological data about the first farm animal, and the firstfarm animal is in a herd; a second sensor tag generating behavioral dataof the first farm animal; a third sensor tag adapted to be worn by orimplanted, ingested, or inserted into a second farm animal in the herdof the first farm animal, the third sensor tag generating physiologicaldata about the second farm animal contemporaneous with the physiologicaldata about the first farm animal; a fourth sensor tag generatingbehavioral data of the second farm animal that is contemporaneous withthe behavioral data of the first farm animal; a processor in electroniccommunication with the first, second, third, and fourth sensor tags, theprocessor programmed to determine whether the first farm animal isdistressed based on the physiological data about the first farm animal,the behavioral data of the first farm animal, the physiological dataabout the second farm animal, and the behavioral data of the second farmanimal.

In an aspect, a system for monitoring or managing livestock on a farmmay include an in vivo sensor adapted to generate biological dataregarding a parameter of an animal at a sensing interval; an externalwearable device that communicates wirelessly with the in vivo sensor,the external wearable device receiving the biological data at a relayinterval and relaying it to a remote location, wherein the externalwearable device provides non-contact power to the in vivo sensor; and aprocessor at the remote location in electronic communication with theexternal wearable device that receives the biological data regarding theparameter of the animal at a communication interval, the processorprogrammed to: (i) assess a health risk for the animal based on thebiological data generated by the in vivo sensor, and (ii) generateinstructions to modify at least one of the sensing interval or the relayinterval of the in vivo sensor, or the communication interval based onthe health risk. The in vivo sensor may be one of ingested, implanted,or inserted. The external wearable device may be a sensor tag associatedwith the animal or a different animal. The biological data may relate toat least one of a temperature, a glucose level, a hormone level, a bloodgas, an oxygen level, a blood chemistry, a pH, or an analyte. The relayinterval may be an interval at which the in vivo sensor transmits thebiological data regarding the parameter of the animal to the externalwearable device. The communication interval may be an interval at whichthe processor receives the biological data regarding the parameter ofthe animal. The processor may be further programmed to generateinstructions to determine if the health risk has decreased and togenerate and transmit instructions to the in vivo sensor to decrease atleast one of the sensing interval or the relay interval if the healthrisk has decreased. The external wearable device may be releasablyattached to a mount permanently affixed to the animal. The externalwearable device may also generate data indicative of at least one of amovement, a behavior, and a physiological parameter of the animal. Thebehavioral data may relate to at least one of a grazing habit, a grazingpattern, a feeding duration, a rumination, a drinking habit, a migrationpattern, a sleeping schedule, a lying time, a reproductive activity, acongregation activity, or a proximity to another animals/stationarydevice. The processor may further generate instructions to modify atleast one of a sensitivity or a communications power of the in vivosensor based on the health risk. The processor may generateinstructions, the processor is further programmed to consider at leastone of a predicted in vivo sensor battery life, a mesh networkperformance, a received signal strength of the external wearable device,a proximity of the animal to a point of interest, a proximity to asuspected break in containment, or a desire to activate an actuator inproximity to the animal with the in vivo sensor.

In an aspect, a method for monitoring livestock on a farm may includeproviding an in vivo sensor to be one of ingested by, implanted in, orinserted in an animal; generating data of a parameter of the animal at asensing interval when the in vivo sensor is ingested by, implanted in,or inserted in by the animal; electronically transmitting the data ofthe parameter of the animal at a relay interval to an external wearabledevice that relays the data to a server at a communication interval;with the server, determining a health risk of the animal based on thedata of the parameter of the animal; with the server, generatinginstructions to modify at least one of the sensing interval or the relayinterval based on the health risk; and modifying at least one of thesensing interval or the relay interval by transmitting the instructionsto the in vivo sensor. The external wearable device may be a sensor tagassociated with the animal or a different animal. The data may relate toat least one of a temperature, a glucose level, a hormone level, a bloodgas, an oxygen level, a blood chemistry, a pH, or an analyte. The relayinterval may be an interval at which the in vivo sensor transmits thedata regarding the parameter of the animal to the external wearabledevice. The communication interval may be an interval at which theserver receives the data regarding the parameter of the animal. Themethod may further include, with the server, generating instructions todetermine if the health risk has decreased and to generate and transmitinstructions to the in vivo sensor to decrease at least one of thesensing interval or the relay interval if the health risk has decreased.The external wearable device may be releasably attached to a mountpermanently affixed to the animal. The external wearable device may alsogenerate data indicative of at least one of a movement, a behavior, anda physiological parameter of the animal. The behavioral data may relateto at least one of a grazing habit, a grazing pattern, a feedingduration, a rumination, a drinking habit, a migration pattern, asleeping schedule, a lying time, a reproductive activity, a congregationactivity, or a proximity to another animals/stationary device. Themethod may further include, with the server, generating instructions tomodify at least one of a sensitivity or a communications power of the invivo sensor based on the health risk. The method may further include,with the server, generating instructions to modify at least one of thesensing interval, the relay interval or the communication interval basedadditionally on at least one of a predicted in vivo sensor battery life,a mesh network performance, a received signal strength of the externalwearable device, a proximity of the animal to a point of interest, aproximity to a suspected break in containment, or a desire to activatean actuator in proximity to the animal with the in vivo sensor.

In an aspect, a computer-implemented method to conserve an in vivosensor's power may include (a) training a model with a training data setfor predicting a condition of an animal and obtaining a trained model,wherein the training data set comprises known outcomes associated withbehavioral data and health data for a plurality of animals; (b)predicting a condition of an animal by inputting current behavioral dataand current health data to the trained model; and (c) based on thepredicted condition, tailoring a parameter of an in vivo sensor thatdetects biological data of an animal and relays it to an externalwearable device. The parameter may be at least one of a sensitivity, asensing interval, a relay interval, a communications interval, or acommunications power of the in vivo sensor. The parameter may be furthertailored based on at least one of: a predicted in vivo sensor batterylife, a mesh network performance or a received signal strength of theexternal wearable device. The parameter may be further tailored based onat least one of: a proximity of the animal to a point of interest or aproximity to a suspected break in containment. The parameter may befurther tailored based on a desire to activate an actuator in proximityto the animal with the in vivo sensor.

In an aspect, a method of configuring a mesh network for monitoring andmanaging livestock, the mesh network comprising a plurality of radionodes, wherein at least one of the plurality of radio nodes is attachedto an animal may include providing an augmented reality user interfacethat presents content and a real world view of a geographical area withone or more topographic features; for each radio node of the pluralityof radio nodes, identifying a potential placement site in thegeographical area; evaluating a performance of the mesh network bypredicting the performance of the mesh network, the mesh network havingthe potential placement site for each radio node, based at least on theone or more topographic features and data regarding the animal;utilizing the predicted performance to generate a recommended placementsite for each radio node of the plurality of radio nodes; anddisplaying, with the augmented reality user interface, the recommendedplacement site for each radio node of the plurality of radio nodes ascontent overlaid with the real world view of the geographical area.

In an aspect, a system to determine whether a farm animal is distressedmay include a first sensor tag generating behavioral data or positiondata of a first farm animal, wherein the first farm animal is in a herd;a second sensor tag generating behavioral data or position data of asecond farm animal in the herd of the first farm animal, wherein thebehavioral data of the second farm animal is contemporaneous with thebehavioral data of the first farm animal; a contextual sensor to collectcontextual data relating to the first farm animal and the second farmanimal; and a processor in electronic communication with the first andsecond sensor tags and the contextual sensor, the processor programmedto determine whether the first farm animal is distressed based on thebehavioral data or position data of the first farm animal, thebehavioral data or the position data of the second farm animal, and thecontextual data.

These and other systems, methods, objects, features, and advantages ofthe present disclosure will be apparent to those skilled in the art fromthe following detailed description of the preferred embodiment and thedrawings.

All documents mentioned herein are hereby incorporated in their entiretyby reference. References to items in the singular should be understoodto include items in the plural, and vice versa, unless explicitly statedotherwise or clear from the text. Grammatical conjunctions are intendedto express any and all disjunctive and conjunctive combinations ofconjoined clauses, sentences, words, and the like, unless otherwisestated or clear from the context.

BRIEF DESCRIPTION OF THE FIGURES

The disclosure and the following detailed description of certainembodiments thereof may be understood by reference to the followingfigures:

FIGS. 1A and 1B depict schematics of the system.

FIG. 2A depicts a schematic of an animal mount.

FIG. 2B depicts a schematic of an object mount.

FIG. 2C depicts a schematic of a sensor tag.

FIGS. 3A-3B depict a planning and installment system

FIGS. 4A-4D depict a registration process.

FIG. 5 depicts a process for identifying an animal's status.

FIG. 6 depicts an environment in which an animal may determine itslocation.

FIGS. 7A-7B depict an ad-hoc mesh network supported by smart phones.

FIG. 8 depicts a tag 102 acting as a repeater.

FIG. 9 depicts a process for operating a power-efficient ad-hoc meshnetwork.

FIG. 10 depicts a process to conserve a wearable sensor's power.

FIG. 11 depicts a process to infer a condition of an animal in a herd.

FIG. 12 depicts a system and process to locate an animal.

DETAILED DESCRIPTION

Regarding FIGS. 1A-1B, a system for animal tracking and management 100in an agricultural location, such as a farm, is depicted. Althoughreferences to a farm will be made throughout this application, it shouldbe understood that this is for exemplary purposes and should not be seenas limiting applications to a farm. The disclosed methods and systemsare also applicable for other agricultural settings such as ranches,small backyard applications, as well as when animals are located awayfrom the farm, such as on the road, at a show, and the like. It willalso be apparent to those skilled in the art that the disclosed methodsand systems may be applicable in other nonagricultural settings,especially those facing similar challenges to the small farm, includingasset tracking, monitoring or management in an environment havingaspects that may interfere with communication such as moving or livinganimals or irregular contour of land or space.

In reference to the Figures throughout this description, all two-wayarrows indicate communication between or among components of the system.For readability not all communication pathways are shown in all figures.The absence of a line between two components does not indicate theinability of the components to communicate with one another, or a lackof association. Thus, data exchanged in the embodiments described hereinhas a plurality of paths, both direct and indirect. Also, communicationbetween components described herein is meant to encompass both two-wayand one-way communication. The communication may be direct or indirectvia an intermediate device, such as for example, a repeater.

There may be a plurality of beacons 120, distributed around areas ofinterest on a farm, broadcasting information, such as locationinformation (including location), beacon ID, and the like. The broadcastlocation may be received by a plurality of sensor tags 102 (alsoreferred to as wearable sensors, animal worn radio devices or nodes). Asensor tag 102 may be associated with an animal mount 110 and incommunication with one or more repeaters 104, other sensor tags 102,beacons, communication devices 114 which interact with gateways 108 tocommunicate with a processor or remote server 122 located in the cloud118. References to communication devices 114 are meant to encompasssmart phones 114 and the terms may be used interchangeably. Referencesto a processor are meant to encompass one or more processors orprocessing units. A processor programmed to perform the functionalityherein may be located in remotely as described herein, but a processormay be located in any one of the components of the system describedherein including but not limited to gateways, object mounts, animalmounts, beacons, tags, mobile devices, communications devices, sensors,nodes, and radio devices. References to a remote server 122 should notbe considered limiting and may include a plurality of processors, aplurality of servers, a cloud computing platform, or cloudcomputing-based services. A remote server 122 may interface with adatabase 130, artificial intelligence and machine learning platforms.Additional farm sensors 124, which may be optionally connected with anobject mount 126, may be attached to farm equipment and infrastructure.Additional sensors may be in vivo sensors 128 which relay informationthrough a tag 102 attached to the same animal or a nearby animal. Theadditional farm sensors 124 may be in communication with the one or moresensor tags 102, the repeaters 104, gateways 108, smart phones 114, andthe like to communicate information to and receive information from theremote server 122. There may be a communications device 114 such as amobile phone, tablet, and the like, which may communicate with theremote server 122 as well as with the gateways 108, repeaters 104, tags102, animal mounts 110, object mounts, actuators 112, and the like. Theremote server 122 may include or be in communication with a database 130having information including an association between an individual animalmount 110 or object mount and a sensor tag 102 and physical, positionaland behavioral data associated with the individual animal, the object,and the like.

Each beacon 120 may regularly transmit (advertise) location information,unique identification information, data message, and the like, which isutilized by tags 102 and communications devices (smart phones) 114within range. A beacon 120 broadcast may be used to establish a locationfor a tag 102 or proximity of a tag 102 to an interesting location orobject, without the need for triangulation, multilateration, other morepower-intensive technologies (e.g., GPS), and the like. When fixed, abeacon's 120 location may be known to a remote server 122 located in thecloud 118 and stored in a database 130 in communication with the remoteserver 122. In this way, received beacon 120 broadcasts provide locationcontext for data received from a sensor tag 102. The placement ofbeacons 120 may be designed such that the location of any sensor tag 102within range may be bounded and a sensor tag's 102 location may bedetermined with greater precision for places of particular interest(e.g., Sensor Tag “A” is close to Feeder “1”). A beacon 120 maybroadcast fixed information at a fixed interval, or broadcast contentand interval may be set or changed by a user using a communicationsdevice 114 or by the remote server 122 using the cloud 118 connectivityprovided by the communications device 114 or gateways 108 or repeaters104. In an illustrative and non-limiting example, the transmission rateof a beacon 120 may be increased (transmission interval decreased) sothat less power is required for sensor tag(s) 102 to detect the beacon120 because they will be able detect the beacon 120 more quickly (thusthey won't have to scan for such a long period) as there is a smallertransmission interval. In an illustrative and non-limiting example, thetransmission rate of a beacon 120 may be decreased in a location (suchas a barn) when the animals are known to be elsewhere (e.g. out atpasture).

Referring to FIG. 2A, an animal mount 110 may include a rugged animalattachment mechanism 203 to secure the animal mount 110 to the animal.There are a plurality of implementations or classes of animal mounts 110where the geometry of the animal attachment mechanism 203 for attachingthe animal mount 110 to the specific animal type is determined for aparticular type of livestock, such as cattle, horses, sheep, goats,birds, and the like. There may be multiple placement locations fordifferent mounts such as an ear tag, rear leg pedometer, upper tailring, and the like. In embodiments, the animal mount 110 including areleasable tag attachment mechanism 212 may be incorporated directlyinto a device or accessory used with an animal such as a halter, bridle,saddle, blanket, turnout sheet, identification collar with or withoutcounterweight, bell boot, and the like. Design considerations for themount 110 and animal attachment mechanism 203 may include placementlocation for effective sensing, animal comfort/tolerance, the abilitymaintain position/orientation over time, resistance to animal damagesuch as rubbing or rolling, resistance to loss, suitability for desiredsensing, and low hazard to the animal. The releasable tag attachmentmechanism 212 may be any of a snap, a latch, a rivet, a magnet, arelease buckle on a webbing strap, a hook-and-loop fastener, anadhesive, interference fit mechanism, or the like.

An animal mount 110 may comprise a releasable tag attachment mechanism212 and a passive RFID device 211 that may include a passive mount ID204, a mount processor 208, and a mount passive transceiver and antenna210. While references will be made throughout to RFID it should beunderstood that this is intended to be representative of a variety ofcontactless, radio wave identification technologies such as NFC(Near-field communication) which operates at 13.56 MHz at ranges of lessthan 20 cm, animal identification tags operating at 120-150 kHz with arange of 10 cm, and the like. The animal mount 110 may have an optionalanimal attachment mechanism 203 if it is not built into an animalaccessory. The animal mount 110 may have optional visiblecharacteristics 202 such as color, unique serial number, identificationinformation such as farm ID information, and the like. In embodiments,the optional visible characteristics 202 such as color and placement ofthe animal mount 110 may be indicative of the type of the animal ratherthan unique to that specific animal (such as a unique serial number). Inan illustrative example, animal mounts 110 that have animal attachmentmechanisms that are similar (or even identical) but which are used indifferent applications (e.g., Dairy Cow ear tag vs. Beef Cattle ear tag)may be visually distinct (e.g., using color) to aide in identification.Attachment location (e.g. right ear vs. left ear) may be indicative ofanimal gender. The animal attachment mechanism 203 may be designed so asto permit the use of industry-standard animal attachment mechanisms suchas ear tagging methods, accessories and tools. Ear tags are prevalent indairy, beef, goat, and sheep operations and the geometry of tags fromvarious manufacturers are very similar and incorporate geometry that isfield proven for durability.

The passive mount ID 204 may include a unique serial number, farm IDinformation, type of livestock, intended placement location on theanimal, gender, breed, and the like, which may be accessible to acommissioning device (e.g., an RFID device) as discussed elsewhereherein, using the mount passive transceiver and antenna 210. Inembodiments, the animal mount 110, passive mount ID 204, or both mayalso provide the tamper-resistant animal traceability required by foodsafety legislation. In embodiments, the passive mount ID 204 may beprogrammed with data regarding the animal at the time of installation ofthe animal mount 110 on a specific animal.

The mount processor 208 may control the interface between the passivemount ID 204 and the mount passive transceiver and antenna 210. A subsetof the information in the passive mount ID 204 may be provided by thirdparty organizations such as the Meat and Livestock Association (MLA),required by government regulations, and the like.

A releasable tag attachment mechanism 212 may be designed to secure thetag 102 in such a way that, while the tag 102 may be easily releasedfrom the releasable tag attachment mechanism 212 and replaced with a newtag 102, the tag 102 is secure even in challenging environments (e.g.extreme temperatures, mud, snow, water, physical abrasion, and the like.In some embodiments, the tag attachment mechanism 212 is designed suchthat a tag 102 may be removed and easily replaced without removing theanimal mount 110 from the animal. The tag attachment mechanism 212 maybe designed so as to permit the use of industry-standard animalattachment mechanisms such as ear tagging methods, accessories andtools. Ear tags are prevalent in dairy, beef, goat, and sheep operationsand the geometry of tags from various manufacturers are very similar andincorporate geometry that is field proven for durability. The tagattachment mechanism 212 may be common across a plurality of classes ofanimal mounts 110 such that a universal tag 102 may be used in multipleclasses of animal mounts 110.

Referring to FIG. 2B, an object mount 126 may be similar to an animalmount 110 but rather than an animal attachment mechanism 203, an objectmount 126 may have an object attachment mechanism 205. There may be aplurality of classes of object mounts 126 having different objectattachment mechanism 205 designed to attach to different types ofobjects and infrastructure such as tractors, tools, saddles and othertack, water troughs, mangers, gates and the like. A tag attachmentmechanism on an object mount 126 may have the same geometry as a tagattachment mechanism 212 of an animal mount 110 such that a tag 102 maybe used in either an animal mount 110 or an object mount 126. The objectmount 126 may also include a passive RFID device 211 which may includean object ID 207, a processor 208 and passive transceiver and antenna210. The object mount 126 may interact with a tag 102 as describedelsewhere for an animal mount 110 and a tag 102 may be associated withthe object mount 126 in a manner similar to that described elsewhereherein for an animal mount 110.

Referring to FIG. 2C, a tag 102 is a small, battery powered device whichoperates for an extended period (months or years) and may be replacedwhen its battery is depleted. A tag 102 may include one or more sensors214, such as position, orientation, posture, motion, ambienttemperature, acceleration, physiological data/parameters of the animal(such as internal temperature, heart rate, respiration and the like),gas sensors, microphones, and the like. A tag 102 may include a tag ID219, a passive transceiver and antenna 220, an active transceiver andantenna 222, a processor 218, memory 216 and a primary(non-rechargeable) battery 224. In embodiments the above transceiversand antennas 220 222, processors 218 and memory 216, one or more sensors214 may be separate components, integrated in a single component, orsome variant thereof. Forgoing a replaceable battery and serviceableenclosure enables a robust, single-piece assembly without joints orfasteners and facilitates a tag 102 that is impervious to water anddirt, at a lower cost. A tag 102 may communicate data acquired by thesensors 214 using the active transceiver and antenna 222 through themesh network or a mobile device back to the remote server 122. The tag102 may store data from sensors 214 and then transmit the data when aspecified volume is reached, at a specified frequency or when a sensor214 reading crosses a specified threshold. A first tag 102 may receivetransmissions from other tags 102 and sensors 124, 128 (FIG. 1B). Thefirst tag 102 may forward the received data from the other tags 102 andsensors 124, 128 to a repeater 104, a gateway 108, a communicationsdevice 114, and the like. The other tags 102 or sensors 124, 128 may belocated on the same animal but have a reduced signal due to location onthe animal (e.g. an ear tag might have a better signal than an anklet)or animal stance (e.g. an ear tag may not have a better signal when theanimal's head is down). Also, in some cases, animals may have internal,in vivo sensors 128 such as a reticulorumen bolus (in reticulum), tailhead inject, and vaginal bolus with a limited communication range. A tag102 may also forward data from the in vivo sensors 128. A sensor tag 102may transmit time, location data, positional or stance data (a headposition, a body position, a body elevation, a movement, or a stance) aswell as data from its sensors as well as other in vivo sensors 128, farmsensors 124, data from other sensor tags 102, and the like. Positionalor postural data may be based on one or more of an inclinometer, anaccelerometer, gyroscopes, barometric pressure sensor, a GPS sensor, andthe like which may be located in a tag 102, or an in vivo sensor 128. Insome embodiments, a tag 102 may comprise an emergency locater which mayinclude direct cellular or satellite connectivity and/or GPS capability.Tags 102 with an emergency locator option may be reserved for valuableanimals, herd leaders (where the location of the herd might bedetermined based on the location of the herd leader), animals which arefrequently off site such as horses (such as part of a rider downsystem).

A tag 102 may be associated with a specific, permanent, animal mount 110or object mount 126 as described elsewhere herein (FIGS. 2A-2B). The tag102 may be configured to provide specific functionality upon beingassociated with a particular class of animal mount 110 or object mount126. When the battery 224 inside a tag 102 becomes depleted, the tag 102may be removed from its mount 110, 126 and discarded. A new tag 102 maythen be affixed to the animal mount 110 or object mount 126 andcommissioned as disclosed elsewhere herein. Because an animal mount 110or object mount 126 may already be associated with a specific animal orspecific object, a new tag 102 may be immediately activated, and datafrom the new tag 102 correctly merged with data from the previous tag102 without further user action as described elsewhere herein.

A farm sensor 124 (FIGS. 1A-1B) may include one or more sensing devicesand a communications module. A farm sensor may or may not beconfigurable (similar to the tag 102) depending on the specificapplication. A farm sensor 124 may measure and report one or moreparameters that may provide context for interpreting data from a tag102. Sensing devices may measure ambient weather such as temperature,humidity, wind, light levels, and the like, as well as weight, thepresence or absence of an objects, water level/flow, feed level, feedrelease tripped, and the like. A farm sensor 124 may detect the state ofequipment (fans, etc.), position of gates and doors, occupancydetectors, ambient light levels, noise, dust, gas (e.g., ammonialevels), and the like

In an illustrative and non-limiting example, a farm sensor 124 may beassociated with a water container “A” may measure water levels in thatcontainer and may then report 2.5 gallons. After a tag 102, associatedwith Horse “1,” is detected near water container “A” a new report of 2gallons may be received. The remote server 122 may then determine thatit is probable that Horse “1” consumed 0.5 gallons of water.

In embodiments, a farm sensor 124 may accept user input to mark eventssuch as stall cleaned, feed delivered, and like or to track workflowsteps. In embodiments, a farm sensor could use a change in a measurablequantity to track workflow steps without direct user involvement.

In embodiments, a farm sensor 124 at a fixed location may furtherinclude a beacon broadcasting information such as location, beacon ID,and the like which may assist in determining the proximity of a tag 102to the farm sensor 124 may be established. In embodiments, a farm sensor124 may be associated with a mobile object, such as a trailer, tractor,saddle, and the like, and may sense parameters related to the locationor operation of such an object.

An actuator 112 may perform an action in response to a command from theremote server 122, direct activation by a nearby device such as a tag102, communications device 114, and the like. An actuator 112 may be amechanical device such as a latch, a door, a valve, and the like. Anactuator 112 may be an electrical output such as dry contact, voltageoutput, and the like to generate a farm indicator to signal animals orpeople, activate an appliance such as a fan or heater, and the like. Afarm indicator may be a visual, audible, or haptic indicator. In anillustrative and non-limiting example, a cooling fan may activate onlywhen an animal tag is detected in the vicinity, reducing energy use.

A repeater 104 (FIGS. 1A-1B) may include one or more, specializedwireless transceiver(s) capable of communicating with tags 102 and farmsensors 124, simultaneously operating an ad-hoc mesh network, includingrebroadcasting the received data to reach a gateway 108 or acommunications device 114 capable of transmit data from different tags102 and farm sensors 124 to a remote server 122. Repeaters 104 andgateways 108 may be line-powered or use a combination of solar panelsand one or more batteries. The physical locations of repeater 104 may beknown to the remote server 122 and/or database 130 such that thelocation of a tag 102, communication device 114, and the like may beestimated by knowing the distance, angle, or both to three or morerepeaters 104 or gateways 108 in range. In some embodiments a repeater104 may also act as a location beacon 120 and broadcast data regardingits location, either specific geographic location or logical location,such as “Pasture 1”, or unique identification.

A gateway 108 (FIGS. 1A-1B) may act as a data collector for the facility(e.g. farm). There may be a limited number of gateways 108 at a specificfacility. A gateway 108 communicates, via a mesh network, with tags 102,sensors 124, 128, repeaters 104 and acts as a communication relaybetween these and a remote server 122 located in the cloud 118. Inembodiments, a gateway 108 may also act as a beacon 120 and/or arepeater 104. In embodiments, a gateway device may be mobile, such as aon a vehicle or trailer, or transportable to extend system functionalitywhile in transit or at off-premises events such as a fair, an auction,an expo, a horse show, and the like.

Communications between tags 102, sensors 124, 128, repeaters 104,gateways 108, and communication devices 114 may leverage one or more lowpower, short range wireless protocols such as ZigBee, Thread, Bluetooth,Z-wave, 6LoWPan and the like. The communication protocol may be selectedsuch that the range is sufficient to transmit data across anticipateddistances such as across a pasture. In embodiments, the system may use acombination of Bluetooth Low Energy (BLE) and Thread/Zigbee to leveragenew transceivers that support both technologies concurrently. In anillustrative and non-limiting example, the BLE range should besufficient to transmit data from a tag on an animal to a repeater on afence line of a field. The repeater could, in turn, use the same ordifferent technology to transmit this data to the gateway orcommunication devices. In embodiments the communication protocol may beselected such that components may also communicate with communicationdevices 114 that happen to be in range, such as smart phones, that maybe carried individuals on the farm or at off-farm events as describedelsewhere herein.

In embodiments, a gateway 108 may communicate with the cloud 118 usingany number of internet protocols such as Cellular, Ethernet, WiFi,Satellite, private terrestrial links, and the like. A gateway 108 mayrequire only very a low data rates and may tolerate long connectionintervals enabling the use of low cost, low power, wireless protocolssuch as LTE CAT M1, NB IoT, or LoRa, and the like. The gateway mayfurther reduce communications demands by batching transmissions frommultiple tags to the cloud, caching relatively static cloud data,adapting communications intervals based on cloud feedback, and the like.

Repeaters 104 and gateways 108 may advertise their presence, health,relative to availability, and the like. They may elect to interconnectand form an ad hoc mesh network to which tags 102 and farm sensors 124may connect. The ad hoc mesh network may be used to send informationbetween the cloud 118 or a remote server 122 and one or more tags 102,farm sensors 124, actuators 112, and the like. One or more gateways 108may communicate information between a remote server 122 having a systemapplication 132 and/or a communications device 114 having a systemapplication 134 via the cloud 118 and tags 102, farm sensors 124,beacons 120, actuators 112, communication devices 114, and the like. Thesystem applications 132, 134 may be accessed on a remote server 122 or acommunications device/smart phone 114 respectively. In some embodiments,a network sniffer may be installed as part of the system to monitor theperformance of the mesh network.

When presented with multiple communications paths to the remote server122 or cloud 118, tags 102, farm sensors 124, beacons 120 and actuators112 may be adapted to use the least-costly or most power-efficientoption. In an illustrative and non-limiting example, a tag on a farmwith multiple beacons 120, repeaters 104 and a gateway(s) 108 maycollect location information from the beacons and repeaters, but use anearby communications device to send data to the cloud, therebyeliminating the need for repeaters to relay the message and a gateway toinitiate a new data transmission that may incur per-byte charges.

In embodiments there may a system using a mesh network sniffer andperformance prediction to facilitate the planning and provisioning of amesh network comprising beacons 120, repeaters 104, gateways 108, tags102 and the like through awareness of natural and man-made topographicfeatures (hills, stone walls, valleys, wallows, ponds, watering holes,tree lines, shrubs, forested areas, and the like), field layout, andexpected animal/animal tag 102 distribution. The system may allow aninstaller and/or the farmer to visualize performance challenges andadjust node placement accordingly.

Referring to FIGS. 3A-3B, a planning and installment system 300 mayinclude a mesh network sniffer 302 to identify existing nodes 304,signal strength and coverage for the existing nodes 304, and a mobileand/or cloud based application 306 (which may be a part of the systemapplications 134) in communication with the remote server 122 and systemapplication 132. The application 306 may include a user interfaceaccessible via a graphical display. The user interface may accept userinput from a variety of input devices including voice activation,keyboard, touchscreen, movement of a mobile device in space and thelike. The application 306 may be aware of the geographic topography of alocation, and, using the graphical display, may provide a visualdepiction of a geographic area by displaying a 2-dimensional map or3-dimensional visualization, such as location aware augmented reality orvirtual reality, comprising topographic features of the geographicalarea including geographic contour lines 308, manmade features 310 suchas stone fences, buildings, and the like. The application 306 may guidea farmer through a site survey, evaluate performance and recommendoptimum placement, and then guide the farmer through installation ofcomponents.

The application 306 may allow for a user to annotate fences, fieldlayout, field purpose (pasture vs. crops) which may impact coverageneeds. The application 306 may allow for a user to provide additionalinformation regarding anticipated performance challenges such asmaterials for manmade objects, anticipated areas of animal congestionsuch as field entrances, feeding stations, anticipated average number ofanimals, anticipated species of animals in a given field, average massfor different species or types of animals, anticipated animal roamingand herd behavior, average number of tags 102 and placement/location ofa tag(s) 102 on animals of different species, and the like. There may bea database documenting signal obstruction profiles for different typesof materials (e.g. wooden building are less obstructive than metalbuildings), and thickness or volume (e.g. obstruction based on animalsize, animal species, and the like).

Further, the application 306, may enable entry of an anticipated numberof animals 314 to understand their impact on network performance.Animals 314 and the anticipated tag signal strength 318 from tags 102associated with the mobile animals 314 may also be shown as an overlayin the application 306. Additionally, fencing of animals may result incontrol points/pinch points in the flow of the animals which may be usedto assist with the placement of receivers. Large numbers of animals orlarge congregations of animals may indicate the need for additionalnodes to provide adequate coverage due to the absorption properties ofthe animals, relocation of a subset of the animals to another location,change in placement of nodes on the animals (ear tag vs. bell boot), orthe like.

Further, the application 306, may enable entry of the desired locationaccuracy for different areas of the farm. For instance, in an openpasture, location accuracy to within 10 meters may be sufficient,whereas near feeding stations, an accuracy of 1 m may be desired. Theapplication 306 may enable entry of geo-fencing constraints to limitcertain types of animals to certain fields such as pasture vs. cropfields, bull separated from cows, and the like based on the animal's tag102 data.

This information may be used to optimize positioning of new nodes, whileminimizing the required number of additional devices, such as beacons,120, repeaters 104, gateways 108 and the like. The application 306 mayinclude a map manager that, based on the information, may determine aplurality of radio nodes needed to provide appropriate cover andidentify proposed placement sites on the map for each of the determinedradio nodes. The map manager may further identify potential signalobstructions on the map given the geographic topography, manmadeobstructions, anticipated animals and animal distribution. Theapplication 306 may include a prediction facility which may evaluate apredicted network performance given the proposed placement sites, thetopographic features over which the network must extend, and dataregarding the animals. For example, anticipated areas of animalcongestion may have both higher levels of signal obstruction due toanimal mass (average mass may vary with animal species/type) as well asa higher number of tags 102 (average number of tags per animal may varywith animal species or be entered by user) which may act asmini-repeaters passing data from tag 102 to tag 102 if no repeater 104or gateway 108 is within range. Knowing the animal species may provideinsight regarding whether they are prone to herding and herdingbehavior, (e.g. are all the animals in a field likely to be within acertain distance of one another, close to a specific topographic featuresuch as a wallow, or evenly distributed through the field). Knowing alikely orientation for the animals (e.g. cattle tend to align theirbodies in a N/S orientation when grazing or resting) may provideinformation regarding how the amount of obstruction created by theanimals might vary depending on the direction of signal propagationbeing modeled (e.g. would the signal be passing longitudinally throughthe animal or side to side)? Knowledge regarding the animal speciesmight alter possible vertical range of obstruction (e.g. cattle arebigger and taller than sheep, goats and sheep are similar in size, butgoats climb so may provide obstruction over a great vertical range). Theanimal species may also impact the expected magnitude and duration ofcertain signal impairments (e.g., the difference between the resting andgrazing height off ground level for a horse is much greater, say, than asheep. A ruminant species will take long regular breaks during grazingfor the chewing/regurgitation cycle, during which time the head will beerect and an ear-mounted sensor will experience less signal attenuation.The prediction facility may then use the predicted performance toreoptimize the proposed placement of the new radio nodes and generate arecommended placement site for each of the new radio nodes. Theapplication may further display each of the new radio nodes at theirrecommended placement site as an overlay to the topographic map.

The application 306 may allow a user, using the user interface, to alterone or more potential or recommended placement sites. Alterations mayinclude an alteration to the number, species, geographic location, andthe like of the animals on the farm. Alterations may include alteringthe number or location of tags 102 on the animals. The application 306may then reevaluate the performance of the mesh network and determinewhether the mesh network is resilient to the altered placement(s) sitesproposed by the user. The application 306 may then reevaluate theperformance of the mesh network and determine whether the mesh networkis resilient to the changes regarding the animals.

The map manager and prediction facility may include algorithms tocalculate optimum placements of repeaters 104, gateways 108, and beacons120 given current mesh performance, topography, anticipated animals, andthe like. The consideration of geographic topography facilitates thesystem's ability to suggest node placement to maximize coverage whileminimizing the number of nodes. In some embodiments, the map manager andprediction facility may include or access machine learning to improvethe algorithms based on data documenting changes in mesh performancewith changes in the number and/or placement of network nodes. The systemmay include obtaining feedback data including mesh network performanceand radio node coverage after new radio nodes have been positioned asrecommended as well as documenting the animal distribution and the timeof data collection. A subset of the feedback data may be used a trainingset for the machine learning to improve the models and algorithms usedby the map manager and prediction facility. In some embodiments theselection of the subset of the feedback data may be partially based onidentifying instances where the performance exceeded an anticipatedthreshold. In some embodiments a subset of the feedback data may be usedas a testing data set to verify the model and algorithms.

The application 306 may include access to maps, surface and aerialphotographs, and other geospatial data, and of the location over which,network performance, fence lines, place names, geographic fencing may beoverlaid. Maps, photography, and geospatial data may be imported fromthird party providers, imported from drone video, imported from athird-party 3D mapping device, such as a LiDAR device, import of iPhonedistance sensing and video, and the like. A user of the application,such as a farmer, may annotate the map with additional information suchas fence lines, pasture edges, if not obvious, farm borders, boundaries,place names, existing network nodes, and the like. A user of theapplication 306, such as a farmer, may annotate the map with additionalinformation about manmade objects such as a fence line, a feeder, atrough, a waterer, a farmhouse, a pole, a barn, a henhouse, a shed, ashelter, a corral, a pasture, a field and the like. This may be used toidentify pinch points, areas in need of higher bandwidth, and areaswhich require greater location accuracy, such as gateways betweenpastures, feeding stations and the like. This information may be used tofacilitate the placement of repeaters 104, gateways 108, beacons and thelike.

The application 306 may include display modules 316 for displaying themaps (both 2D maps and 3D interactive AR/VR visualizations) of thefacility disclosed above and elsewhere herein with overlays showingnetwork signal strength and coverage 305, locations of identified nodes304, and the like, enabling a farmer or installer to easily identifygaps, potential signal obstructions and adjust proposed or actuallocation of devices. Overlays may include anticipated network signalstrength and coverage 315 for proposed nodes 312 and areas with poorcoverage due potential signal obstruction from identified topological ormanmade obstructions. Information obtained from the mesh network sniffer(active or passive) may be overlaid on the topographical map todetermine if the node placement is resilient to topography (e.g. arethere zones where the coverage may be poor due to potential signalobstruction from intervening fences, hills, large congregations ofanimals, and the like). As beacons 120, repeaters 104, and gateways 108are placed/positioned the system may verify the predicted coverage andsignal strength using the mesh network sniffer and the application toview the coverage provided. The new nodes may be placed by the user,placed by an algorithm to maximize performance, and the like. Potentialplacement sites may include one or more of a fence line, a feeder, atrough, a waterer, a farmhouse, a pole, a barn, a henhouse, a corral, apasture, a field and the like. Information may be collected on theresulting mesh network performance using either a hand held networksniffer or by fixed location sniffer node 136. This informationregarding the new node placement(s) and resulting network performancemay be fed back into a machine learning system to improve futurepredictions.

The system may also be used to update the mesh network as new pastures,buildings and the like are added. The system may also be used to updatethe mesh network based on livestock changes. The system may allow a userto enter proposed alterations or changes to the topography such as theaddition of a pond or changes to manmade objects, such as the additionor removal of a building. The system may allow a user to enteredproposed or anticipated changes to the number and type of animals,changes to tagging, and the like. The changes to the topography andlivestock may also result in additional changes such as different animalcongregation behavior given a new pond, of changes in gate placement.Given the alterations, the system may then reevaluate the performance ofthe mesh network (signal strength and coverage) and provide the userwith a map displaying anticipated changes as a result of the proposedchanges to topographic features, manmade features, livestock, and thelike. These may display as additional representations of coverage or asan overlay of display showing the current coverage. In embodiments theinformation regarding anticipated impact on network coverage may be usedto facilitate placement of the proposed new feature (such as siting of anew watering system). In embodiments, the system may be designed tosuggest alterations (within a specified range) of the placement of thenew feature so as to optimize coverage. The system may be used totrouble shoot the mesh network.

In embodiments, electric fences, which are already commonly used foranimal containment in animal husbandry, may be leveraged to providegreater mesh network coverage with lower power consumption (higher powerefficiency). Electric fences operate on pulsed discharges of 2 kV-10 kVdelivered by a central fence “charger” where discharges occur once everyfew seconds. Referring to FIG. 9, in embodiments, at least two nodes(wireless transceivers), such as repeaters 104 and/or gateways 108, arepositioned near an electric fence which is energized in accordance witha pulsed discharge interval (step 902). The at least two nodes maydetect the pulsed discharges (step 904) using non-contact means such asby detecting the change in the electromagnetic field due to the pulseddischarge. The nodes may synchronize wireless transmissions using thepulsed discharges as a heartbeat signal to create a shared transmissiontiming window (step 908). One or more of the repeaters 104 and gateways108 may operate in a low power mode until awakened by the magnetic fieldgenerated by the pulsed discharge. Upon waking up, the device may sendand receive data and then return to the low power mode. When the timingis not inside the shared timing window, at least one of the at least twonodes enters a low power mode where transmitting or receiving is notpossible (Step 912). In embodiments, one or more of the repeaters 104and/or gateways 108 may be powered by energy harvested from the electricfence. The intermittent nature of the transmissions may reduce powerconsumption compared to operating continuously. Because thefence-synchronized nodes are more power-efficient, they may be lesscostly due to the need for less batteries, smaller solar panels, and thelike. The smaller field devices may be less prone to animal damage aswell. As an added benefit, loss of the synchronization pulse by a givennode may indicate fence damage, which could be then reported to the farmoperator. Upon loss of the synchronization pulse, the affected node(s)may discontinue transmissions or may revert to a continuous transmissionmode. In embodiments, as part of the response at a node to the loss ofthe synchronization pulse would be to switch to synchronouscommunications and send an alert of a possible electric fence breakageto the application. In embodiments, the application would detect thechange in transmission from the node (e.g. loss of communications withthe node or change to continuous transmissions by the node). Upondetermining that a node has been affected, an alert of a possibleelectric fence breakage may be triggered. In configurations withmultiple nodes synchronizing on the same fence line pulse, the loss ofmultiple nodes might be the basis for a higher confidence determinationof a possible electric fence breakage or better pinpoint the location ofthe issue.

In embodiments, when the alert is triggered, the application mayidentify one or more animal tags whose last or most recent locationswere in the vicinity of the affected node or the electric fence with thepossible breakage. The application may allocate additional resources intracking (e.g., increase a tracking frequency, increase transmit power,acquire higher fidelity motion or heading date from the tag's sensors)on the one or more identified animal tags. The increased tracking mayenable early detection of potential movement of the animals out of oneor more pastures associated with the electric fence with the possiblebreakage and aide in speeding recovery of the escaped animal.

In embodiments, when a tag 102 loses connectivity, whether due to abreak in the electric fence or other reasons such as the animal escapingand moving out of range of the network, those tags having emergencylocator functionality (e.g., GPS and/or cellular components) may beconfigured to begin transmitting their location and behavioral data(e.g., motion, heading, vital signs) to the remote server 122 usingtheir cellular connection.

In cases where a farm has multiple fence chargers, for example servingdifferent pastures, one repeater 104 or gateway 108 may be attached atto each fence circuit and set to operate in a bridging node. Thesebridging devices would operate during both communications timing windows(or in an always-on mode) and would store and forward to the other fencecircuit's devices during its respective awake interval. The bridgingdevice would receive & transmit messages during a first communicationstiming window (when devices synchronized to a first electric fence arepowered up and communicating). Then, during a second communicationstiming window (when devices synchronized to a second electric fence arepowered up and communicating) the bridging device would transmitmessages received during the first communications timing window, andreceive new messages. Thus, messages from the two sets of synchronizeddevices are bridged.

A generic tag 102 may be common between all animals and potential mount110 locations. Once an animal tag 102 is installed in a mount 110, itneeds to be configured for the specific animal type and mount location.For example, the local and cloud processing algorithms may be differentfor a cow vs. a sheep. The motion processing for a collar worn animaltag 102 may be different than the motion processing for a leg-mountedanimal tag 102. A different data transmission rate may be desirable foran animal tag 102 for a horse than the data transmission rate desiredfor a sheep.

Rumination detection is applicable for a cow, but not for a horse, whichis a non-ruminant species. In embodiments, a mount 110 may bepre-programmed with data identifying an animal type/desired animal tag102 configuration. In embodiments, a mount 110 may be programmed withdata identifying an animal type/desired animal tag 102 configuration atthe time the mount 110 is attached to an animal.

Mount 110, 126 identification and association with a tag 102 (andrelated tag 102 configuration) may be performed once upon attachment ofmount 110, 126 and/or installation of the tag 102 to the mount 110, 126.This may be done using a smartphone 114 or similar device which has auser interface, cloud connectivity, an exciter device to energizepassive ID tags on both the mount 110, 126 and the tag 102, and theability to read the tag 102 and mount 110 passive IDs. The followingreferences to smart phones are not meant to be limiting and encompassany device similar to that described above.

Upon installation of the tag 102 to the mount 110, 126 the RFID antennasof each are positioned such that both antennas will be in range suchthat they may be read simultaneously by an exciter device such as asmart phone. In embodiments, this may be done before the mount 110 isinitially attached to the animal. In embodiments, this may be done afterthe mount 110 is attached to an animal, such as when replacing adepleted tag 102.

Referring to FIGS. 4A-4D, a registration process 400 is depicted. Asmart phone, or other exciter device running an animal managementapplication, may be positioned near the mount 110, 126 and tag 102 suchthat IDs 204, 207, 219 for both may be read (step 402). The activationof the passive tag ID 219 on the tag 102 may cause the tag 102 to wakefrom a low power sleep mode (step 404). Using the mount IDs 204, 207 andtag IDs 219, the animal management application may associate the tag ID219 and the mount ID 204, 207 (step 408) and send the associated tag ID219 and mount ID 204, 207 to an associated user account on the remoteserver 122 (step 410). The association of the tag ID 219 and the mountID 204, 207 may be automatic, or the association may be entered by auser of the animal management application or a combination thereof. Thetag 102 and mounts 110, 126 may then be associated with a specificanimal or object and user (step 412) and stored in the user's database(step 414) which is accessible by the animal management application. Aremote server 122 in communication with the animal managementapplication and the database may then send configuration information tothe tag 102 based on the mount IDs 204, 207 (step 416). The two-stepprocess of sending the association to a remote server 122 which thenprovides the configuration information to the tag 102 allows the tag 102to be configured specifically for the animal or object to which it isattached.

Referring to FIG. 4B, the configuration process (step 416) is depicted.An animal mount ID 204 may be unique to an animal and may includeinformation regarding animal species, gender, status (breeding, heifervs. cow, in-training vs. retired, mounting location, and the like. Anobject mount ID 207 may include information about the type of object(trough, saddle, and the like).

Upon waking up the tag 102 contacts the remote server 122 (step 418)using the active transceiver and antenna 222. The connection may be madeusing one or more nodes on the mesh network such as repeaters 104,gateways 108, a smartphone 114, a remote location node as describedelsewhere herein, relay through other tags, and the like. In embodimentsthe remote server 122 sends configuration information (step 420) to thetag 102 based on information in the mount ID 204, 207. The configurationinformation may be sent over the mesh network, by way of a smart phone,and the like as described elsewhere herein.

The configuration information may be based on an animal type, an animalgender, an age of the animal, a weight of the animal, a feedingprotocol, a medication protocol, a health status, an owner, a plan ofcare, and the like. The configuration may be based on an equipment type,an operator training required, a maintenance interval, an instructionmanual, a point of contact, an owner, and the like. The configurationinformation may include sensors to activate, algorithms for processingsensed date (e.g. motion processing algorithms), data collectionfrequency for a sensor, thresholds for different sensor values,different parameters to sense (min, max, running average),communications intervals, and the like, so as to maximize tag 102effectiveness while minimizing power consumption. In an illustrativeexample, a horse may have a different motion sensing threshold than acow.

The tag 102 may store the sensed data and send a log of the stored dataat a communications interval which may be set as part of theconfiguration or a default interval. In some instances, based on analgorithm, the tag 102 may identify that a threshold is crossed, asensor is out of range, the animal is in distress, and the like,whereupon the tag 102 may communicate the data immediately or at adifferent frequency (e.g. more frequently if the animal is showing signsof stress).

An estimated location may be provided as part of the data communicatedby the tag 102. A tag 102 may use one of several strategies fordetermining its location and may modify its own behavior based on thedetermined location, update the cloud with the determined locationinformation, and the like. The location of the tag 102 may be determinedbased on proximity to a communications device 114, such as a smartphone,with a known location. The location of the tag 102 may be determinedbased on proximity to a beacon 120. If a sufficiently strong signal isseen from a point of interest beacon 120 the location of the beacon maybe used. For example, proximity to a water trough beacon may be used tolocate the animal at the water trough. When the location is based on asingle known location such as a communications device 114 or beacon 120,a confidence circle, based on the signal strength between the tag 102and the communication device 114 or beacon 120 may sent along with thelocation.

The tag 102 may multiliterate or triangulate based on signal strengthand/or angle of arrival, respectively, for signal from at least threebeacons. Calculation of location based on these methods may be performedlocally on the tag 102 or the data may be sent to the remote server 122for calculation, or the calculation of location may be performed locallyand or at the remote server to different degrees of accuracy, forexample based on computing resource, communications bandwidth required,or power constraints. In some instances, the tag 102 may identify signalstrength or angle of arrival for repeaters and send this information tothe remote server 122 for calculation of the tag 102 location. Thephysical geospatial location of the repeaters may be stored in thedatabase. Additionally, the geospatial location of repeaters may bestored locally and send as part of the broadcast.

The estimation of location may take into account additional informationsuch as animal and herd behavior. For example, calculating the locationof an animal when its head is down (e.g. grazing) may result in highlevels of uncertainty due to the RF absorption by the ground and pasturegrasses impacting estimated ranges from the anchor locations. Referringto FIG. 12, in an illustrative example, anchor antennas 304 may belocated approximately 2 meters off the ground along multiple fencelines. Tags 102 may be positioned on the top of a horses 1206 head (thepoll) on a halter. When “idle” or chewing a horse's head will be erect1206A and a head tag 102 would be approximately 1.5 meters off theground. In the head erect instance, path loss generally follows a freespace RF path model and the estimates may locate that horse in arelatively small area 1204. However, when grazing a horse's head will bedown 1206B, resulting in the tag 102 being located approximately 0.5meters off the ground. This results may result in the signal beingsignificantly attenuated by the ground and pasture grasses. If the pathloss is calculated using a free space RF model the performance may besignificantly degraded by the unaccounted-for RF absorption from theground and pasture grasses and the resulting estimates may locate thathorse in relatively large area 1202. However, if a model that accountsfor the additional RF absorption from the ground (such as by using aplane-earth (PE) path loss model, examples of which are known in theart) is used, the estimated position may be more precise with theestimate locating the in a relatively small area 1204. In some instance,depending on the horse's 1206 orientation relative to a particularanchor antenna 304 (e.g. if the horse's body is between an anchorantenna 304 and a tag 102 on the horse's lowered head), the signal maybe further attenuated by the horse's body and the resulting estimate maylocate the horse in a larger error or the error may be larger in thedirection of the obstructed anchor 304.

A tag 102 may be replaced when needed without the loss of historicaldata and without the need to remove the mount from the animal. A tag mayneed replacement due to battery depletion, failure, loss, damage,availability of an improved version, and the like. The current tag 102may be removed from its mount 110, 126 and a new tag 102 attached to themount 110, 126, and then, as described above, a smart phone or otherexciter device is used to read the IDs 204, 207, 219 of the mounts 110,126 and the tag 102. As shown in FIG. 4C, when the remote server 122receives the information relating the mount ID 204, 207 and the new tagID 219, the ID for the previous tag 102 is unlinked from the mount ID(step 430) in the database while the data from the old tag 102 remainsassociated with the mount ID 204, 207. The ID for the new tag 102 isthen linked with the mount ID 204, 207 (step 432) and stored in the userdatabase (step 434).

The remote server 122 send configuration information, based oninformation associated with the mount ID 204, 207 to the new tag 102(step 438). Going forward, data from the new tag 102, is stored with thedata from the previous tag 102 associated with the same mount ID 204,207 and all of the stored information may be used by the remote server122 in evaluating the status of an animal, an object, or the like.Because the remote server 122 may already have information on thespecific animal or object the configuration of the new tag 102 may becustomized. For example, if the remote server 122 is aware that thespecific animal to which the associated mount ID 204 is attached, forexample Bessie, is at 270 days of gestation, the type and frequency ofmonitoring configured may be different or more frequent than a nongravidcow.

A remote server 122 may include or access one or more machine learningsystems to infer a condition of an animal, including identifying animaldistress, based on data from the animal's tags 102 (behavioral data andphysical/vital data), location awareness, external data sources (sun,weather, and the like), herd behavior, status of farm infrastructure andthe like. The machine learning system(s) may develop a model forpredicting a condition of an animal using a training data set ofbehavioral data, vital signs, health data, locational data, herdbehavior, contextual data and external sources correlated with knownoutcomes. Behavioral data may include grazing habits, grazing patterns,a feeding duration, a rumination, drinking habits, migration patterns,sleeping schedules, lying times, reproductive activity, congregationactivities, proximity to other animals or a stationary device such aswatering trough, nesting box, and the like. In embodiments, thebehavioral data may be processed on the tag 102, or inferred fromcollected data at the remote server 122. Contextual data may be obtainedfrom farm sensors 124, for example, temperature and humidity sensors,wind speed, and the like and external data sources such as times forsunset/sunrise, weather forecasts, moon phase, and the like. Animalbehaviors and relevance of those behaviors to detection of distress maybe dependent on local conditions, season, and the like. For example, ahorse will typically like down (lateral or sternal recumbency) to sleepfor at least a portion of each day. Provided 24-hour access to pastureduring the sunny growing season, a horse will typically lie down duringdaytime sunny periods and graze continually at night. The cycle istypically reversed in winter. A laterally recumbent horse in a pastureon a sleeting winter day would be an indicator of distress. Otherbehaviors, such as not laying down when other animals are laying, may beignored in the analysis of distress as certain animals engage in asentinel activity, keeping watch over other resting animals.

Behavioral data and health data, including vital signs and physiologicalparameters may be obtained from sensors, such as in vivo, implantable,or ingested sensors, as described elsewhere herein, such as includingtemperature, heart rate, respiration rate, glucose level, bloodpressure, oxygenation, and rumen movements. In some embodiments, an invivo sensor 128 may have an on-board power source and send data throughthe tag using an active transmitter. In some embodiments, as shown inFIG. 4D, an in vivo sensor 128 may be passive (without its own powersource and/or active transmitter). An external sensor tag 102 maytransmit a signal to an in vivo sensor 128 (step 430) using short rangefrequencies (120-140 kHz) such as might penetrate the animal to connectwith the in vivo sensor 128. This may energize the in vivo sensor 128(step 432) and read the in vivo data (step 434). The in vivo sensor 128may be read using short range frequencies such as might penetrate theanimal to connect with the in vivo sensor 128. In some cases, vitalsigns may be inferred using non-invasive sensors to detect bodilyfunctions such as urination, respiration, lactation, a bowel movement,passing gas, a body measurement, a calving activity, and the like, allof which may be indicators of health. In an illustrative andnon-limiting example, a non-invasive sensor may detect CH4 or H2S, whichwould indicate that an animal is passing gas. Lack of a CH4 or H2S mayindicate a possible bowel obstruction. In an illustrative andnon-limiting example, a non-invasive audible sensor may detect normalgut sounds and alert to a possible obstruction when such sounds cease.

In embodiments, an initial training data set may be based on third partydata or historical data. Referring to FIG. 11, in embodiments, thesystem may obtain behavioral and health data for a plurality of animalsin a herd (step 1102) and select a subset of the behavioral data andhealth data and known outcomes (step 1104) for use in training a modelto predict a condition of a selected animal in the herd (step 1108). Thetraining set may be revised based on data obtained for the particularsite and assets (animals and farm equipment). After training the model,a trained machine learning model may comprise one or more algorithmssuch as linear regression algorithms, regularized linear regressionalgorithms, decision tree algorithms and subtypes of any of thealgorithms thereof. The trained model may then be able to predict acondition of a selected animal in the herd based on health andbehavioral data for that animal. In embodiments the trained model may betested to validate the model using a new set of testing data based onknown outcomes and behavioral and health data obtained from the herd. Inembodiments, a training set may further include environmental data andknown outcomes associated with the environmental data. The trained modelmay use current environmental data together with the current health andbehavioral data to predict a condition of a selected animal.Environmental data may include a temperature, a humidity, aprecipitation, a pollen count, an air quality, a weather event, aseason, a sunrise, a sunset, or a solar irradiation, and the like. Inembodiments, a training set may further include contextual data andknown outcomes associated with the contextual data. The trained modelmay use recent contextual data together with the recent health andbehavioral data to predict a condition of a selected animal. Contextualdata may include a location or a path the animal is on or has traversed.In embodiments, the behavioral data and current behavioral data mayrelate to a whole herd behavior such as a whole herd respiration rate,and the like which may be averaged or normalized.

The machine learning model may accessible from a user interface on asmart phone 114, remote server 122, or other device using an API. Theuser interface may enable a user to identify groups of correlatedanimals whose data might be used as input in the determination of agiven animal's condition. In embodiments, a user may indicate members ofa herd having similar characteristics (age, gender, common recentevents, and the like). In embodiments, the machine learning model mayidentify appropriate “herd” members based on determined proximitybetween at least two animals and at least one of (i) the data of thephysiological parameter of the first animal, (ii) the data of thebehavioral parameter of the first animal, (iii) the data of thephysiological parameter of the second animal, or (iv) the data of thebehavioral parameter of the second animal. In embodiments, the machinelearning model may identify appropriate “herd” members based on commoncharacteristics, location, and the like. For animals which are membersof the same herd, the remote server 122 and/or the machine learningmodel may calculate whole herd behavior parameters such as an a wholeherd respiration rate, an average respiration rate of the whole herd,averages over the herd of the individual behavioral data of herd memberssuch as a grazing habit, a grazing pattern, a feeding duration, arumination, a drinking habit, a migration pattern, a sleeping schedule,a lying time, a reproductive activity, a congregation activity, or aproximity to another animal, other herd members and/or a stationarydevice. For example, if all but one of a herd's members are grazing andhave respiration rates within one standard deviation of the mean. If theone member of the herd's motion also indicates grazing (not running),yet their respiration is two standard deviations above the mean for theherd, this herd member may be at a greater risk of distress.

A remote server 122 may receive data from a plurality of tags 102, farmsensors 124, actuators 112, and the like via a gateway 108, acommunications device 114, a repeater 104 an actuator 112, and the like.The remote server 122 may model behavior in relationship to the timingof farm workflow (e.g., 1 hour after feeding). The remote server 122uses the model on current behavioral and health data of the individualanimal and the herd to identify a present state, a future state, trends,and the like. The model may also include contextual data includingenvironmental data such as one or more of a temperature, a humidity, aprecipitation, a pollen count, an air quality, a weather event, aseason, a sunrise, a sunset, a phase of the moon cycle, a solarirradiation, and the like. The environmental data may include currentdata obtained from farm sensors or from third parties. The environmentaldata may include forecast data such as storm warnings, heat waves,extreme cold, and the like. Based on the analysis, the remote server 122may initiate an action with an actuator 112, alert a farmer or trainer,and the like as described herein.

In an illustrative example, farm animals tend to follow fixed routinesand the remote server 122 may use machine intelligence to identify thesetrends such as learning characteristic path patterns for a herd and/orindividual animals. For example, sudden departure of the herd's grazingpattern from recent history could indicate presence of a predator. Afemale deliberately staying apart from the group may be ready to givebirth.

In an illustrative example, a first sensor tag 102 associated with afirst farm animal may provide physiological data about the first farmanimal in a herd and a second sensor tag 102 may provide behavioral dataabout the first farm animal. A second farm animal, in the same herd asthe first farm animal, may have a third and fourth sensor tag 102providing physiological and behavioral data about the second farmanimal. The data from the first and second farm animals may be collectedcontemporaneously or may be collected asynchronously. The remote server122 may use the physiological and behavioral data from both the firstand second farm animals, in addition to other data described elsewhereherein, such as contextual or observational data, to determine whetherthe first farm animal is distressed.

In embodiments, predications from a remote server 122 may be used toupdate a status display, or status data for a particular animal orobject. In embodiments, a predication, a status, or a health riskassessment may result in an alert or notification to a farmer, using anapplication, text message, and the like on a communication device 114.In embodiments, predications, a status, or a health risk assessment froma remote server may result in commands to actuators (refill trough, turnon ventilation fan or heater), updates to beacons, updates to taginstructions (e.g. change in reporting frequency), and the like. It maybe possible for a farmer to specify how and under what conditions inwhich an alert or notification should be sent or an action performed.For example, if a health risk increases, a sensing and/or reportingfrequency and/or a broadcast power for a sensor tag 102 (eitherassociated with that animal or associated with another animal in theherd) may be increased. If the level health risk decreases, a sensingand/or reporting frequency and/or a broadcast power may be decreased toextend sensor tag 102 life. The remote server 122 may send commands toeffect the change using the ad hoc mesh network, a mobile device, andthe like.

Referring to FIG. 5, upon receipt at the remote server 122 of data froma sensor tag 102 (step 501), an entry may be created in a database 130(step 502) documenting data. Creation of the entry may includere-synchronizing individual data entries transmitted in a batch to theactual time of each measurement. Contextual information such as locationinformation may be appended to the recently created entry (step 504).Location information may be part of the received data or determined atthe remote server 122 as disclosed elsewhere herein. In embodiments,additional contextual data may be associated with the entry (step 506).The additional contextual information may include external environmentaldata, the identification of nearby animals, the identification of nearbyobjects (e.g. trough), and the like. In embodiments, a farmer, worker orother observer may use the application 306 to add observational entriesinto an animal's record directly. For example, with horses, a user maybe able to select observed behavior from options such as “biting side”,“eating”, “lying down” and the like. This observational data may bestored with the other entries associated with an animal and used forfuture analysis.

For an animal, data from farm sensors may be associated with an animalentry based on proximity to the sensor (e.g. change in water level maybe associated with a nearby animal), status of farm infrastructure inproximity to an animal, state of equipment (fans, etc.), position ofgates and doors, occupancy detectors, ambient light levels, noise, andthe like. Essentially, data regarding anything that could affect thebehavior of the animals or contribute to modelling for the developmentand verification of behavior tracking algorithms, and the like. Datafrom external sources may include current and predicted informationregarding weather, temperature, humidity, heat index, cold index,storms, tornados, sunrise, sunset, and the like.

The remote server 122 may proceed to analyze the composite data (step508) including animal species and location of tag(s) on the animal, theanimal's current location (e.g. in the barn, in a field, on a trailer,and the like), time of day, weather, and the like. The remote server 122may use the analyzed data together with recent past behaviors, pathtracking compared with other nearby animals of the same species and withhistoric data from the same animal to evaluate an overall risk profile(step 510) for the given animal. based on. Based on the results of therisk analysis, the remote server may then take an action (step 512).

If an animal is determined to be at risk as a result of the riskanalysis, an alert may be pushed to a remote communication device suchas a farmer's mobile phone based on the specific animal, class of animaland application specific rules. For example, if recent events andbehaviors for a specific animal correlate with the known symptoms of acommon or previously identified ailment, an alert may be sent providingthe identification and location of the animal, the suspected ailment(s),and a list of the behaviors or events on which the alert was based.

In embodiments, the results of the risk analysis may be used to tailor arate of energy consumption by tag(s) 102 associated with the animal. Inan illustrative example, a risk analysis indicating a change in level ofrisk may result in sensor tag(s) 102 being reconfigured to change asensor sensitivity, a sampling frequency, a reporting frequency, aneeded location fidelity, a transmitting power, and the like. In anillustrative example, if the risk analysis indicates an elevated levelof risk associated with the animal, one or more sensor tag(s) 102 may bereconfigured to increase one or more of: sensor sensitivity, a samplingfrequency or measurement interval, a reporting frequency orcommunication interval, a needed location fidelity, a transmittingpower, and the like, the need to monitor the animal more closelyjustifying the increased battery drain. In another example, if the riskanalysis shows that the animal is generally healthy and the level ofrisk is low, the tag(s) 102 may be reconfigured to extend battery life,such as by decreasing sensor sensitivity, decreasing sampling frequency,decreasing needed location fidelity, decreasing reporting frequency,decreasing transmitting power, and the like.

The need to monitor the animal more closely may be based on a trainedmodel to predict a condition of an animal. Referring to FIG. 10, themodel may be trained with a training set having known outcomesassociated with behavioral data and health data for a plurality ofanimals (step 1002). The trained model may then predict a condition ofan animal based on input of current behavioral and health data of theanimal (step 1004) and determine a level of risk associated with theanimal. Based in part on the predicted condition and the level of risk,one or more sensor tag(s) 102 parameters may be tailored or reconfigured(step 1008) to change one or more of: a sensor sensitivity, a samplingfrequency or measurement interval, a reporting frequency orcommunication interval, a needed location fidelity, a transmittingpower, and the like.

In embodiments the changes to the configuration may include a schedulingelement such as increasing reporting frequency at night and decreasingreporting frequency during the day when the farmer or other workers maybe about. In embodiments, changes to one or more of: sensor sensitivity,a sampling frequency or measurement interval, a reporting frequency orcommunication interval, a needed location fidelity, a transmittingpower, and the like may be based on at least one of a predicted wearablesensor battery life, a mesh network performance or received signalstrength of the wearable sensor. In embodiments, changes to one or moreof: sensor sensitivity, a sampling frequency or measurement interval, areporting frequency or communication interval, a needed locationfidelity, a transmitting power, and the like may be based on proximityof the animal to a point of interest or proximity to a suspected breakin containment. In embodiments, changes to one or more of: sensorsensitivity, a sampling frequency or measurement interval, a reportingfrequency or communication interval, a needed location fidelity, atransmitting power, and the like may be based on at least one of: adesire to activate an actuator in proximity to the animal wearing thesensor, a change in the status of an animal (e.g. before/after birthing)or a suspected breach in containment.

The reconfiguration instructions to the tag(s) may be provided by amobile device in electronic communication with a sensor tag 102associated with the animal and the one or more sensor(s) 214 of thesensor tag 102 and/or an in vivo sensor 128 communicating through thesensor tag 102.

A tag 102 may use one of several strategies for determining itslocation, and may modify its behavior based on the identified locationand/or update the remote server 122 with its location information. Thetag 102 may identify currently available methods for determininglocation such as 1) proximity to a smartphone 114 with known location,2) proximity to a fixed location beacon 120 (i.e., a Bluetooth beacon),3) proximity to another tag 102 (animal or object) which has a highconfidence in its own location, or 4) by triangulating from a set of atleast three location anchors 602 at known locations. Each method poses adifferent burden on battery life, so, under certain circumstances aselsewhere, a tag 102 may choose to accept a lower fidelity locatingmethod to conserve battery life. Other factors may be used in selectingwhich method to use including a time since the radio device was lastlocated, a confidence in the location of the radio device, a currentcondition of the animal bearing the radio device, or a time of day.

Referring to FIG. 6, in some circumstances, a mathematical processcalled multilateration or trilateration may be used to estimate locationusing the signal strength from three or more wireless transmitters atknown locations, called anchors 602. These anchors may be beacons 120,repeaters 104, or gateways 108 located on fences 604 or buildings 606.To determine a location, a tag 102 may listen for signals from theanchors 602 and record at least one of the signal strength or the anglefor each anchor 602 it sees. For multilateration or trilateration towork, a given animal tag must be able to hear transmissions from atleast three anchors, however if more anchors are seen, this informationcan be used to improve the accuracy of the location estimate. Inembodiments, the tag 102 forwards signal strength data, angle ofarrival, or both for all of the anchors 602 identified. Thetrilateration routine running on the remote server 122 performs thelocation estimation given the relative locations of the anchors and thesignal strength, angle of arrival, or both. In embodiments, a tag 102may have some knowledge, even at a relatively lower fidelity, regardingapproximate anchor 602 locations and the tag 102 may choose to transmitinformation about only a subset of the anchors 602 seen, to reduce datatransmitted and power use.

Because errors may accumulate the further away a given anchor 602 isfrom tag 102 being located, preference may be given to signal strengthin selecting the three anchors 602 and in the weighting of differentanchors 602 in the model. Additionally, the relative angles of arrivalor known coordinates of anchors may be used in selecting anchors 602 toprovide a distributed group which bounds the location of the tag

Triangulation and multilateration accuracy and stability may be affectedby a large number of variables. It may require some level of de-noising(filtering) prior to presenting to a user. Animals often exhibitcharacteristic gates, tracks of movement, finite limits on instantaneousacceleration, and biological limits to their motion which can beincorporated into animal and even species-specific location smoothing.

Location smoothing can also be enhanced by augmenting trilateration withsome information from other sensors (either on the tag 102, in-vivo, onthe animal, or external), including inertial sensors, magnetic field,atmospheric pressure, etc. For example, the magnetic vector (heading)between two moments in time may be forwarded by the to the cloud andfused with the trilateration result to nudge the predicted location inthe right direction, even if, for example, certainty is low. Forexample, an accelerometer, barometer, or the like may be used todetermine whether an animal's head is up or down. This may be taken intoconsideration when determining location as described elsewhere herein.

In embodiments, a tag 102 may use a beacon 120 to identify points ofinterest on the farm (e.g., water trough). If a sufficiently-strongsignal is seen from a beacon 120, there is no need to performtrilateration and the beacon 120 location is used as the tag 102location.

In embodiments where no infrastructure is present and tags 102 are usingone or more smartphone(s) 114 to communicate with the remote server 122,the only location information available is the smartphone's location(presumably known and appended to the data when transmitted) and aconfidence circle, based on the signal strength between the tag andsmartphone.

In terms of power required for each locating approach, the use of thesmartphone(s) 114 location requires none, the identification of a beacon120 require a small amount of power to scan for the beacon (˜500milliseconds), trilateration requires the most power as it has to scanfor multiple anchors (˜5-10 seconds) 602 and send the signal data foreach anchor 602 to the remote server 122. In embodiments, locationaccuracy may be improved with some combination of the three methods.Given advances in processors, it may be more power efficient to performtrilateration locally on the tag 102 if it enables a reduction in thedata to be sent wirelessly. In some cases, running even a low-fidelityversion of the location routine locally can allow the sensor to moreintelligently choose which data to send and which to discard. Dependingon the length of time since the tag 102 was last located, or the remoteserver's 122 confidence in a tag's 102 location, a given tag 102 may beconfigured or intelligently decide which methods should be used and atwhat update rate. The location method and rate may also be adjustedbased on the urgency of the animal's current condition, time of day,etc. to conserve power or bandwidth.

The tag 102 may also ascertain its location relative to other animals(who may be identified using their tag ID 419) and use this knowledge toestablish herd membership (e.g. I am by cow 121) or use collectiveresults and possibly machine learning, to infer and proactively correctfor signal impairments due to other herd members passing between a tag102 and on or more locating beacons 120, repeaters 103 and gateways 108that could be corrected for on remote server 122

Being made mostly of water, animals are excellent absorbers of RFenergy. If another animal is standing between the animal being locatedand the anchor, the signal strength may be severely attenuated, skewingthe result. Because the cloud has location information on estimatedlocation of all sensor tags 102 (animals) in the vicinity, themultilateration calculation can incorporate adjustments to counteractthis signal impairment and improve accuracy. Signal strength to otheranimals, while not fixed anchors, can also be used in a multilaterationcalculation to calculate a location when less than two anchors are inrange, or through iterative approaches be used to refine locations bysubstituting a series of estimated other-animal locations and signalstrengths and identifying locations with strong correlations betweensuccessive calculations. In an illustrative and non-limiting example, asensor tag 102 may identify a proximity of a first radio deviceassociated with a first asset (animal) to a second sensor tag 102associated with a second asset (animal) which has a high confidence inits own location and estimate, based on the proximity and the locationof the second sensor tag 102 and a partial location estimate for thefirst animal (first sensor tag 102) using two or more fixed locationbeacons 120, that the second asset (animal) is obstructing a signalbetween the first sensor tag 102 and other radio nodes such as a fixedlocation beacon 120, a repeater 104, a gateway 108, and the like. Theremote server 122 may determine the transmission impairment of the firstsensor tag 102 based on a location, position or behavioral parameter ofthe second animal. In embodiments, based on the determined transmissionimpairment, the remote server may generate and transmit instructions tomodify transmission characteristics of the mesh network. Theseinstructions may include instructions to one or more sensor tags 102 onthe first animal, instructions to one or more sensor tags 102 associatewith the second animal or other animals in the herd, instructions tonearby beacons 120, repeaters 104 and gateways 108, and the like. Theinstructions may include modifying a transmission rate, modifying apower of a signal transmitted by at least one of the sensor tags 102,modifying how anchors are weighted for a location calculation, and thelike.

In embodiments, based on the determined transmission impairment, alocation of the first radio device may be multilaterated from a set ofat least two location anchors at known locations and a signal strengthof the second sensor tag 102 which has a high confidence in its ownlocation. In embodiments, a refined multilateration calculation for thefirst radio device may include substituting a series of corrected signalstrengths for the obstructed fixed location beacon 120, and identifyingthe location of the first radio device by a strong correlation betweenthe revised estimated location of the first radio device and theproximity of the second radio device.

In embodiments, the correction factor for calculating corrected signalstrength may be based in part on data regarding the posture of the firstanimal. For example, if the first animal has its head down (grazing)when another animal is positioned between it and the obstructed beacon,the impairment will be greater than if its head was erect. Inembodiments, the correction factor for calculating corrected signalstrength may be based in part on data regarding the posture ororientation of the second animal with respect to the path between thefirst animal and the obstructed beacon. For example, a perpendicularorientation may result in a larger portion of the mass of the secondanimal being positioned between the first animal and the obstructedbeacon compared with a parallel or oblique orientation.

For example, the presence of a signal from an adjacent animal suggeststhere may be a 10 m separation between animals. In the example, a cowbetween one animal, 10 m away, and a beacon may cause a 3 dB drop insignal. Thus, 3 dB may be subtracted from one of the beacon signals andthe estimated location may be re-computed. This process may be repeatedfor each beacon. It is then determined which recomputed locations happento place the animal 10 m+/− from the adjacent animal and that locationresult is used going forward.

In embodiments, upon identifying an obstruction based on one or moreanimals, the application 306 may update a graphical representation ofthe mesh network with a representation of the obstruction.

In some embodiments, the method used to determine location of a radionode may be selected based on one or more factors. Method may includedetermining a proximity to a smartphone with a known location,determining a proximity to a fixed location beacon, determining aproximity to another radio device which has a high confidence in its ownlocation, and/or multilaterating/triangulating from a set of at leastthree location anchors at known locations. In embodiments, the locationmay be determined using more than one method to provide increaseconfidence in the determined location. In embodiments, the methodselected may be based on one or more of a time since the radio devicewas last located, a confidence in the location of the radio device, acurrent condition of the animal bearing the radio device, or a time ofday.

Each locating method has the ability to determine a relative confidencein the given location result. The confidence level may be used tographically depict a range of possible locations to the end user. Inembodiments, the relative confidence of different methods of locationmay be fed back to the system (including the tag 102) to cause it toadjust, for future location computations, based on the relativeconfidences, an energy expended for one or more of: determining aproximity to one or more of a smartphone with a known location, a fixedlocation beacon or another tag 102 with high confidence in its ownlocation or triangulating the location.

If a tag 102 is running even a rudimentary multilateration ortriangulation routine, it may be able to determine when its computedlocation places the tag and associated animal outside the boundary of afence line or outside a building. If this animal is assigned to thegiven field or building it now outside, this may trigger the tag 102 toexpend additional energy in subsequent location attempts, change(increase or decrease) a frequency of location updates, or even activatean emergency locating system like GPS.

Maybe even more valuable than absolute location is path awareness. Pathtracking builds on location determination but uses machine learning toidentify and store characteristic patterns for a herd and/or individualanimals. Farm animals follow fixed routines. For example, suddendeparture of the herd's grazing pattern from recent history couldindicate presence of a predator. A female deliberately staying apartfrom the group may be ready to give birth.

The system may also provide monitoring and documentation in a compliancelog of farm workflow and compliance with regulations based on animalbehavior. Farms rely on significant amounts of manual labor and hiringemployees and training them to correctly and consistently perform animalcare tasks may be difficult. The farmer may not always be on site andyet still want to verify animal case such as: animals being turned outto pasture at appropriate times, animals being fed and water at correcttimes, an animal receiving correct medication if needed, horses beingblanketed in inclement weather, animal quarantines being maintained, andthe like. The farmer may also have concerns about equipment andinfrastructure such as whether the barn is adequately ventilated, arethe lights coming on at appropriate times, is the automatic feederworking, is waste or theft occurring, and the like. The farmer may alsowish to track equipment such as saddles, tractors and the like andassure that they are not being ill-used (e.g. left out in the weather,and the like). While this type of tracking might be achieved using anextensive system of sensors and/or cameras on all of the animals andequipment is would be expensive, potentially unreliable and requirelarge amounts of bandwidth. Alternately, tags 102 on animals andequipment may: provide sufficient data to: verify compliance withinstructions regarding animal care; help locate equipment andaccessories; assure that accessories (blankets, fly masks, harnesses,and the like) are on the correct animal; reduce theft; provide historicrecord of an animals care (feeding, watering, pasture time, quarantinehistory, and the like) to demonstrate compliance with animal regulationsand defend against false charges of animal cruelty; proactively ordersupplies and implement repairs, and the like.

Sensor tags 102 may provide data such as animal location, behavioraldata, physiologic data, or positional information regarding an animal orherd of animals from which the remote server may be able to infer aworkflow event. The farmer may be able to enter, using a user interfaceof the application, a series of workflow rules documenting such thingsas what data should be logged in a compliance log, what inferred eventsshould be logged in a compliance, under what conditions should an alertbe issued, under what conditions should an action be initiated. Theworkflow rule relates to at least one of a stabling, a pasturing, aherding, a sheltering, a feeding, a medicating, a provision of water, amanure and/or wastewater removal, an inspection interval, a recordsmanagement, or a feed storage.

In an illustrative example, feed pans containing beacons 120 may includesensors and thus be able to confirm feed is delivered to the correctstall/feeder. In an illustrative example, when the data indicates thelocation of one or more animals in a field, an inferred workflow eventmay be that a particular gate was opened. In another example, when thesensor data indicates that one or more animals are congregating nearother animals and the one or more animals have their heads down, aninferred workflow event may be that the one or more animals were fed. Inanother example, when the sensor data indicates that one or more animalsare near a feed pan containing a beacon, an inferred workflow event isthat a correct food or medication was delivered to the one or moreanimals. The workflow events inferred from the location, behavior orposition of one or more animals may be used to determine compliance witha workflow rule. Workflow events may relate to one or more of astabling, a pasturing, a herding, a sheltering, a feeding, a medicating,a provision of water, a manure and/or wastewater removal, an inspectioninterval, a records management, or a feed storage.

Sensor tags 102 may provide data such as animal location, behavioraldata, physiologic data, or positional information regarding an animal orherd of animals from which the remote server may be able to identify aworkflow event that should be triggered. For example, feeders may dropcorrect food and/or medication based on an identity of a specific animaldetected near the feeder/waterer and the animal's specific status. Forexample, an animal not drinking may be delivered a small portion ofdesirable feed dusted with salt and/or electrolytes to promote thirstand encourage drinking. Additionally, data regarding the timing andamount of food and/or water provided to a specific animal may be used toinfer a condition of an animal.

In some embodiments, a tag 102 may also act as a repeater 104 to relayinformation received from other sources such as other tag(s) 102, invivo sensor(s) 128, and the like to the remote server using the meshnetwork described elsewhere herein.

Because of their low cost and unobtrusive nature, some applications mayuse multiple tags 102 on a single animal (e.g. at tag 102 on the ear anda tag 102 on the ankle) where each tag 102 may be configured to have aspecific sensing objective based on the associated mount 110. Byallocating the work each tags 102 may provide optimal performance. Anankle tag 102 motion sensor no longer needs to detect chewing, and mayuse lower sensitivity and sample rates to save power. Because the ankletag 102 is close to the ground, it may experience worse RF signalimpairment (ground absorption).

However, by detecting its presence on an animal having an ear tag 102(either using local detection methods or via information retrieved fromthe cloud), the ankle tag 102 may choose to forward data (using a lowerpower transmission) to the ear tag 102 and request the ear tag 102 torelay the data from the ankle tag 102 to the cloud 118 and remote server122.

Referring to FIG. 8, a tag 102 acting as repeater is depicted. An animal800 may have one or more mounts 110 with tags 102 as well as one or morein vivo sensor(s) 128. An in vivo sensor 128 may be implanted orinserted into the animal or ingested by the animal. It may be difficultfor an in vivo sensor 128 to transmit data any distance and if a batteryfails it is difficult to replace. In some embodiments, an in vivosensor(s) 128 may have an on-board power source and send data through anexternal sensor tag 102 using an active transmitter. In someembodiments, an in vivo sensor(s) 128 may be passive (without its ownpower source and/or active transmitter) and act as a parasite on theexternal sensor tag's power. An external sensor tag 102 may transmit asignal to the in vivo sensor 128 using short range, low frequencies (forexample 120 kHz-140 kHz) such as might penetrate the animal to connectwith the in vivo sensor 128. This may energize the in vivo sensor 128and allow the vivo data to be read. The external sensor tag 102 may thenforward the data to the remote server 122. The external sensor tag 102may forward configuration information from the remote server 122 to thein vivo sensor 128.

The in vivo sensor 128 may be read using short range frequencies such asmight penetrate the animal to connect with the in vivo sensor 128. Inembodiments, the external sensor tag 102 may be associated with the sameanimal having the in vivo sensor 128 or a different animal.

The use of common communication technology and a mesh network forcommunication between the tag 102 and the remote server 122 may allowfor the creation of an ad-hoc mesh network using smart-phones running acommunication application. An ad-hoc mesh network using smart-phones maybe particularly useful in places the farmer (or animal owner) doesn'tdirectly control such as a horse boarding stable, a county fair, ananimal show, a horse race, and the like. While an individual farmer (oranimal owner) may not control the environment, there may be a number ofsimilarly situated animal owners/farmers present in these locations. Inthese communal environments, the use of their collective smart phones,each having the disclosed application installed, will create a temporarymesh network to provide cloud connectivity for all of them. Referring toFIG. 7A, in an illustrative example, a stable 700 in a remote locationhouses multiple horses 702A, 702B having different owners 704A, 704B.Each horse 702A, 702B has at least one associated tag 102. As the owners704A, 704B move around the stable 700, their smart phones 114A, 114Bwill provide a connection to the remote server 122 for tags 102A, 102Bassociated with horses 702A, 702B owned by either owner 704A, 704B.

A communication platform may forward data from a tag 102 to a remoteserver 122 in the cloud where the data may be processed as describedelsewhere and turned into alerts. In this role, the ad-hoc network doesnot need to know anything about the tag 102, its data, or the associatedaccount. Referring to FIG. 7B, a temporary communication platform 750comprising a plurality of device nodes is shown. Each device node has aradio compatible with that found in the tag 102 and access to the cloud118 and remote server 122. Device nodes include a plurality of smartphones 114A, 114B associated with different owners 704A, 704B running aforwarding application 706, possible gateways 108 having an internetconnection (e.g. WiFi, LAN, cellular, SAT, etc.), possible repeaters 104accessing the network through an alternate network, and other tags 102.As a tag 102 has new information to send, it searches for device nodeson the temporary communications platform, such as a gateway 108, arepeater 104, a smart phone 114 running the forwarding application 706,and the like. Once the tag 102 has identified one or more device nodescapable of forwarding the data, the tag 102 select a device node tocomplete the task, and sends its data to the device to forward to theremote server 122. The device node may be selected based on which devicenode is most likely to complete the task. The tag 102 may prioritize thedevice nodes based on some cost, such as end user's cell phone costs,platform fees, power usage (a smart phone may connect, send anddisconnect more quickly than a repeater allowing the sensor to return tosleep more quickly) and the like. For example, as shown, a tag 102associated with horse 702A (having Owner A) may select the smart phone114B carried by Owner B 704B to transmit data to the remote server 122in the cloud 118.

A tag 102 may detect one or more smart phones 114 and create a detectionrecord of the smart phone which may include one or more of the time ofsmart phone detection, an identity of the smart phone, a location of thesmart phone, or a proximity. The sensor tag 102 may then share thedetection record with the remote server 122. The detected smart phones114 may be associated with accounts other than the user accountassociated with the sensor tag 102. The remote server 122/application306 may use this information to update a map of an ad hoc mesh networkto display to a user. The remote server 122 may use the identified smartphone and its location to estimate a location of the sensor tag 102.

As the temporary communication platform 750 may result in data from atag 102 being transmitted by a third party's device node, privacy andsecurity are very important. Tag 102 data may be privacy protected usingencryption and authentication methods (e.g. MAC checking,challenge-response etc.) A user may access their data by authenticatingtheir application with the remote server 122. The remote server 122 willonly provide data about tags 102 for which the user has the propercredentials. Wireless connections, including that between the tag 102and the temporary communication platform 750 device modes are protectedfrom common attacks (e.g., MITM, replay) using encryption andauthentication methods (e.g., MAC checking, challenge-response, etc.).

In some embodiments, a user may select an option where critical alertsresult in a tag 102 and/or the remote server 122 broadcasting an SOSmessage when a tag 102 identifies an emergency event. In embodiments,the user may elect to have the tag 102 and remote server 122 convey anSOS signal, in the open, to all compatible devices nearby. In anillustrative example, the remote server 122 or tag 102 may alert anynearby users or activate a distress signaling device located on or nearthe animal. In this case, the need for immediate assistance maytemporarily trump privacy.

While only a few embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that manychanges and modifications may be made thereunto without departing fromthe spirit and scope of the present disclosure as described in thefollowing claims. All patent applications and patents, both foreign anddomestic, and all other publications referenced herein are incorporatedherein in their entireties to the full extent permitted by law.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions, and thelike. The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor, or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor, and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions, and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions, and programs as described hereinand elsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions, orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server, and the like. Theserver may include one or more of memories, processors, computerreadable transitory and/or non-transitory media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other servers, clients, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the server. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationswithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, code,and/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient, and other variants such as secondary client, host client,distributed client, and the like. The client may include one or more ofmemories, processors, computer readable transitory and/or non-transitorymedia, storage media, ports (physical and virtual), communicationdevices, and interfaces capable of accessing other clients, servers,machines, and devices through a wired or a wireless medium, and thelike. The methods, programs, or codes as described herein and elsewheremay be executed by the client. In addition, other devices required forexecution of methods as described in this application may be consideredas a part of the infrastructure associated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of a program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code, and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

In embodiments, one or more of the controllers, circuits, systems, datacollectors, storage systems, network elements, or the like as describedthroughout this disclosure may be embodied in or on an integratedcircuit, such as an analog, digital, or mixed signal circuit, such as amicroprocessor, a programmable logic controller, an application-specificintegrated circuit, a field programmable gate array, or other circuit,such as embodied on one or more chips disposed on one or more circuitboards, such as to provide in hardware (with potentially acceleratedspeed, energy performance, input-output performance, or the like) one ormore of the functions described herein. This may include setting upcircuits with up to billions of logic gates, flip-flops, multiplexers,and other circuits in a small space, facilitating high speed processing,low power dissipation, and reduced manufacturing cost compared withboard-level integration. In embodiments, a digital IC, typically amicroprocessor, digital signal processor, microcontroller, or the likemay use Boolean algebra to process digital signals to embody complexlogic, such as involved in the circuits, controllers, and other systemsdescribed herein. In embodiments, a data collector, an expert system, astorage system, or the like may be embodied as a digital integratedcircuit (“IC”), such as a logic IC, memory chip, interface IC (e.g., alevel shifter, a serializer, a deserializer, and the like), a powermanagement IC and/or a programmable device; an analog integratedcircuit, such as a linear IC, RF IC, or the like, or a mixed signal IC,such as a data acquisition IC (including A/D converters, D/A converter,digital potentiometers) and/or a clock/timing IC.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM, and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be configured for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (“SaaS”), platformas a service (“PaaS”), and/or infrastructure as a service (“IaaS”).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (“FDMA”) network or code division multiple access (“CDMA”)network. The cellular network may include mobile devices, cell sites,base stations, repeaters, antennas, towers, and the like. The cellnetwork may be a GSM, GPRS, 3G, EVDO, mesh, or other networks types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable transitory and/or non-transitorymedia that may include: computer components, devices, and recordingmedia that retain digital data used for computing for some interval oftime; semiconductor storage known as random access memory (“RAM”); massstorage typically for more permanent storage, such as optical discs,forms of magnetic storage like hard disks, tapes, drums, cards and othertypes; processor registers, cache memory, volatile memory, non-volatilememory; optical storage such as CD, DVD; removable media such as flashmemory (e.g., USB sticks or keys), floppy disks, magnetic tape, papertape, punch cards, standalone RAM disks, zip drives, removable massstorage, off-line, and the like; other computer memory such as dynamicmemory, static memory, read/write storage, mutable storage, read only,random access, sequential access, location addressable, fileaddressable, content addressable, network attached storage, storage areanetwork, bar codes, magnetic ink, and the like.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the Figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable transitory and/ornon-transitory media having a processor capable of executing programinstructions stored thereon as a monolithic software structure, asstandalone software modules, or as modules that employ externalroutines, code, services, and so forth, or any combination of these, andall such implementations may be within the scope of the presentdisclosure. Examples of such machines may include, but may not belimited to, personal digital assistants, laptops, personal computers,mobile phones, other handheld computing devices, medical equipment,wired or wireless communication devices, transducers, chips,calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers, and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein. All methodsdescribed herein can be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the disclosure,and does not pose a limitation on the scope of the disclosure unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe disclosure.

While the foregoing written description enables one skilled in the artto make and use what is considered presently to be the best modethereof, those skilled in the art will understand and appreciate theexistence of variations, combinations, and equivalents of the specificembodiment, method, and examples herein. The disclosure should thereforenot be limited by the above described embodiment, method, and examples,but by all embodiments and methods within the scope and spirit of thedisclosure.

Any element in a claim that does not explicitly state “means for”performing a specified function, or “step for” performing a specifiedfunction, is not to be interpreted as a “means” or “step” clause asspecified in 35 U.S.C. § 112(f). In particular, any use of “step of” inthe claims is not intended to invoke the provision of 35 U.S.C. §112(f).

Persons skilled in the art may appreciate that numerous designconfigurations may be possible to enjoy the functional benefits of theinventive systems. Thus, given the wide variety of configurations andarrangements of embodiments of the present invention, the scope of theinvention is reflected by the breadth of the claims below rather thannarrowed by the embodiments described above.

What is claimed is:
 1. A computer-implemented method to conserve asensor's power, the computer-implemented method comprising: training amodel to predict a condition of an animal with a training data set,wherein the training data set comprises known outcomes associated withbehavioral data and health data for a plurality of animals; sensing,with a sensor worn by a monitored animal, behavioral data and healthdata of the monitored animal; inputting the behavioral data and healthdata of the monitored animal into the model; predicting a condition ofthe monitored animal with the model based on the inputted behavioraldata and health data; and configuring a parameter of the sensorassociated with the monitored animal based on the predicted condition.2. The computer-implemented method of claim 1, wherein the parameter isa measurement interval of the sensor.
 3. The computer-implemented methodof claim 1, wherein the parameter is a frequency between intervals ofdata transmitted from the sensor.
 4. The computer-implemented method ofclaim 1, wherein the parameter is a sensitivity of the sensor.
 5. Thecomputer-implemented method of claim 1, wherein the parameter is acommunication power of the sensor.
 6. The computer-implemented method ofclaim 1, wherein the parameter is a needed location informationfidelity.
 7. The computer-implemented method of claim 1, whereinconfiguring the parameter is further based on at least one of: apredicted sensor battery life, a mesh network performance, or a receivedsignal strength of the sensor.
 8. The computer-implemented method ofclaim 1, wherein configuring the parameter is further based on at leastone of: a proximity of the monitored animal to a point of interest, or aproximity to a suspected break in containment.
 9. Thecomputer-implemented method of claim 1, wherein configuring theparameter is further based on a desire to activate an actuator inproximity to the monitored animal wearing the sensor.
 10. Thecomputer-implemented method of claim 1, further comprising determining alevel of risk for the monitored animal.
 11. A system for monitoringlivestock on a farm, the system comprising: a wearable mount adapted tobe worn on an animal at a mounting location, the wearable mountcomprising a housing and an RFID device within the housing beingprogrammable with identification data of an animal wearing the wearablemount; a sensor releasably connectable to the wearable mount, the sensorcomprising a memory device and at least one processor, wherein thememory device is programmed to configure the sensor, via the at leastone processor, to be associated with the animal wearing the wearablemount based on the identification data of the animal wearing thewearable mount, and wherein the sensor is adapted to generate dataregarding a parameter of the animal wearing the wearable mount when thesensor is connected to the wearable mount; at least one receiver forreceiving the identification data of the animal wearing the wearablemount and the data regarding the parameter of the animal wearing thewearable mount from the sensor; and an application for monitoringlivestock, in communication with the at least one receiver, theapplication structured to: monitor the data regarding the parameter ofthe animal wearing the wearable mount; predict a condition of the animalwearing the wearable mount based on the data regarding the parameter ofthe animal wearing the wearable mount using a trained model; andconfigure a parameter of the sensor associated with the animal wearingthe wearable mount based at least in part on the predicted condition.12. The system of claim 11, wherein the parameter is at least one of ameasurement interval of the sensor, a frequency between intervals ofdata transmitted from the sensor, a sensitivity of the sensor, acommunications power, or a needed location information fidelity.
 13. Thesystem of claim 11, wherein the trained model is trained with a trainingdata set comprising known outcomes and associated behavioral data andhealth data for a plurality of animals.
 14. The system of claim 11,further comprising a mesh network, wherein the at least one receiver isa node of the mesh network.
 15. The system of claim 11, wherein theapplication is further structured to transmit the configured parameterto the sensor.
 16. A system for monitoring livestock on a farm, thesystem comprising: a first wearable mount adapted to be worn on a firstanimal at a first mounting location, the first wearable mount comprisinga housing and an RFID device within the housing being programmable withidentification data of the first animal; a first sensor releasablyconnectable to the first wearable mount, the first sensor comprising afirst memory device and a first processor, wherein the first memorydevice is programmed to configure the first sensor, via the firstprocessor, to be associated with the first animal based on theidentification data of the first animal, and wherein the first sensor isadapted to generate data regarding a parameter of the first animal whenthe first sensor is connected to the first wearable mount; a secondwearable mount adapted to be worn on a second animal at a secondmounting location, the second wearable mount comprising a housing and anRFID device within the housing being programmable with identificationdata of the second animal; a second sensor releasably connectable to thesecond wearable mount, the second sensor comprising a second memorydevice and a second processor, wherein the second memory device isprogrammed to configure the second sensor, via the second processor, tobe associated with the second animal based on the identification data ofthe second animal, and wherein, the second sensor is adapted to generatedata regarding a parameter of the second animal when the second sensoris connected to the second wearable mount; at least one receiver forreceiving the data regarding the parameter of the first animal from thefirst sensor and the parameter of the second animal from the secondsensor; and an application for monitoring livestock, in communicationwith the at least one receiver, the application structured to: determinea proximity of the first sensor to the second sensor; establish amembership of the first animal in a herd based on the determinedproximity and at least one of: the data of the parameter of the firstanimal, or the data of the parameter of the second animal; monitor thedata regarding the parameter of the first animal and the second animal;predict a condition of the first animal by inputting behavioral data andhealth data of the first animal and behavioral data and health data ofthe second animal to a trained model; and configure a parameter of thefirst sensor based on the predicted condition, wherein the parameter isat least one of a measurement interval of the first sensor, a frequencybetween intervals of data transmitted from the first sensor, asensitivity, a communications power of the first sensor, or a neededlocation information fidelity.
 17. The system of claim 16, wherein thetrained model is trained with a training data set comprising knownoutcomes and associated behavioral data and health data for a pluralityof animals.
 18. The system of claim 16, further comprising a meshnetwork, wherein the at least one receiver is a node of the meshnetwork.
 19. The system of claim 16, wherein establishing the membershipof the first animal in the herd is further based on a location of thesecond animal.
 20. The system of claim 16, wherein configuring theparameter of the first sensor is further based on a desired powerconsumption of the first sensor.
 21. The system of claim 20, whereinconfiguring the parameter of the first sensor is further based on atleast one of: a predicted battery life of the first sensor, a meshnetwork performance, or a received signal strength of the first sensor.22. The system of claim 16, wherein data regarding the parameter of thefirst animal comprises at least one of a movement of the first animal, aphysiological parameter of the first animal, and an animal body functioncomprising at least one of: a urination, a respiration, a lactation, abowel movement, a body measurement, a calving activity, or a passinggas.
 23. The system of claim 16, wherein data regarding the parameter ofthe first animal comprises a behavior of an animal related to at leastone of a grazing habit, a grazing pattern, a feeding duration, arumination, a drinking habit, a migration pattern, a sleeping schedule,a lying time, a reproductive activity, a congregation activity, aproximity to the second animal, or a proximity to a stationary device.24. A system for monitoring livestock on a farm, the system comprising:a wearable mount adapted to be worn on an animal at a mounting location,the wearable mount comprising a housing and an RFID device within thehousing being programmable with identification data of an animal; and asensor releasably connectable to the wearable mount, the sensorcomprising a memory device and at least one processor, wherein thememory device is programmed to configure the sensor, via the at leastone processor, to be associated with the animal based on theidentification data of the animal, and wherein the sensor is adapted togenerate data regarding a parameter of the animal when the sensor isconnected to the wearable mount, wherein the at least one processor isprogrammed to: assess a health risk for the animal based on datagenerated by the sensor; and generate instructions to modify at leastone of a sensing interval of the sensor or a communication intervalbased on the health risk.
 25. The system of claim 24, wherein the sensorgenerates data indicative of a movement or a physiological parameter ofthe animal.
 26. The system of claim 24, wherein the sensor generatesdata indicative of an animal body function comprising at least one of: aurination, a respiration, a lactation, a bowel movement, a bodymeasurement, a calving activity, or a passing gas.
 27. The system ofclaim 24, wherein the sensor generates data indicative of a behavior ofthe animal.
 28. The system of claim 27, wherein the behavior is relatedto at least one of: a grazing habit, a grazing pattern, a feedingduration, a rumination, a drinking habit, a migration pattern, asleeping schedule, a lying time, a reproductive activity, a congregationactivity, or a proximity to another animal or stationary device.
 29. Thesystem of claim 27, wherein the at least one processor is furtherprogrammed to generate instructions to determine if the health risk hasdecreased and to decrease the sensing interval if the health risk hasdecreased.